International Journal of Biometrics (27 papers in press)

Regular Issues

• A Spatial Pyramidal Decomposition Method for Finger Vein Recognition Using Local Descriptors  
  by Badreddine Griouz, Rafik Djemili, Hocine Bourouba, Hakim Doghmane 
  Abstract: Finger vein patterns have been proved as one of the most promising biometric modality for its convenience and security. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. This manner of processing however may not provide optimal recognition accuracies as reported in many studies. Therefore, this paper proposes in the feature extraction stage, the use of the spatial pyramid decomposition (SPD) method aiming at partitioning the finger vein images into increasingly fine sub-regions from which local texture descriptors are obtained. The descriptors adopted in this paper are local binary pattern (LPB), binarized statistical image feature (BSIF) and local phase quantization (LPQ). The performance of the proposed approach evaluated on two publicly databases PolyU and SDUMLA achieves a recognition accuracy higher than that of some existing systems reported in the literature for both the SDUMLA and the PolyU databases.
  Keywords: Finger Vein Recognition; Spatial Pyramid Decomposition; LBP; BSIF; LPQ.
  DOI: 10.1504/IJBM.2020.10026196
   
  
• Multi-pose facial expression recognition using Rectangular HOG feature extractor and Label-Consistent KSVD classifier  
  by Ali Muhamed Ali, Hanqi Zhuang, Ali Ibrahim 
  Abstract: In this paper, a new approach to the classification of facial expressions from multiple pose images is proposed. In this approach, a Rectangular Histogram of Oriented Gradient (R-HOG) algorithm is first designed to extract features of face images. The parameters of the R-HOG algorithm, which is a modification of the original HOG algorithm include cell shape, cell size, block size, and the number of orientation bins. The R-HOG is capable of capturing more discriminative texture features of different facial expressions. In addition, a supervised dictionary learning classifier, the Label Consistent K-SVD (LC-KSVD) algorithm, is adopted to recognize the facial expression of the subject. To investigate its effectiveness, the proposed technique was applied to classify emotional states of the face images in the two public available facial expression datasets: KDFE and RafD. The experiment study showed that the new method outperformed in many aspects those methods reported in the literature tested with the same datasets. First, the new method handles pose variations better. Second, it is more robust in cases where the size of a training dataset is small. Finally, it's accuracy performance is more consistent measured by standard deviations.
  Keywords: Facial Expression Recognition; Emotional Classification; Sparse Coding; Dictionary Learning; Histogram Oriented Gradient; Label-Consistent KSVD.
  
  
• An Accurate and Fast Method for Eyelid Detection  
  by Ahmed A.K. Tahir, Steluta Anghelus 
  Abstract: A novel method called Refine-Connect-Extend-Smooth (R-C-E-S) for detecting eyelids is presented. It consists of four algorithms, Canny edge detector with Prewitt operator, Modified Refine Edge Map (MREM), Connect Edges-Extend (CEE) and Smooth Curve (SC). The method is not based on pre-assumptions that consider eyelids as parabola or lines and it does not use curve fitting algorithm, therefore sever deviation of the detected eyelid curve from the actual eyelid path is avoided. The method is applied to three types of database, CASIA-V1.0, CASIA-V4.0Lamp and SDUMLA-HMT. For CASIA-V1.0 the accuracies are 93.2%, 99.1% and 96.7% for detecting lower eyelid, upper eyelid and free iris and the processing times are 42 ms, 49 ms and 35 ms. For CASIA-V4.0-Lamp these accuracies are 97.6%, 98.3% and 97.8% with processing time 23 ms, 26 ms and 21 ms. For SDUMLA-HMT the accuracies are 95.1%, 95.3% and 96.92% with processing time 35 ms, 40 ms and 31 ms.
  Keywords: Biometics; Canny Edge Detector; Eyelid Detection; Iris Localization; Iris Recognition System; Prewitt Operator; Sobel Operator.
  
  
• New method for identification of persons using geometry foot outline  
  by Khadidja Kafi, Adda Ali Pacha, Naima Hadj Said 
  Abstract: In recent years, identification systems with using biometric features are receiving considerable attention. Iris, palmprint, and footprint are shown as examples. The present study is an attempt to evaluate uniqueness of foot and its use at a possible means of identification using foot-biometric features in face based on foot outline. In order to study this uniqueness, a computer database has been constructed taking 19 right foot outline measurements in centimeters from feet pictures of 102 volunteers(85male and 17 female) using the measuring tool in photofilte. Using the exact measurements and these measurements with
  Keywords: Biometrics; foot outline measurements; personal identification.
  
  
• Biometric Face Classification with the Hybridized Rough Neural Network  
  by Sasirekha Kathirvel, Thangavel K 
  Abstract: Face biometric plays a vital role to authenticate a person in a right way. Face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridized approach based on Rough Set Theory (RST) and Back Propagation Neural Network (BPN) for gender classification using human face images is proposed. It involves four main parts: Pre-processing, Feature Extraction, Feature Selection and Classification. Initially, the images are converted to grayscale and then the median filter is applied to de-noise. The features have been extracted using Local Binary Pattern (LBP) method as they exploit the rich discriminatory information existing in the face images. The evolutionary optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), hybridization of ACO and GA (ACO-GA) and hybridization of PSO and GA (PSO-GA) are investigated for feature selection from the face. Finally, the hybridized Rough Neural Network (RNN) is employed to classify the face images. In this research, experiments have been conducted on real-time face images collected from 155 subjects each with ten orientations using Logitech WebCam and also on ORL face dataset. The experimental result of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with other benchmark classification techniques such as Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN) conventional BPN, and Convolutional Neural Network (CNN) to conclude the efficacy of the proposed approach.
  Keywords: ACO; Biometric Face; GA; Gender; PSO; Rough Neural Network.
  
  
• An Improved Weber Face Based Method For Face Recognition Under Uncontrolled Illumination Conditions  
  by Boualleg AbdelHalim, Deriche Mohamed, Sedraoui Moussa 
  Abstract: This paper presents a new face recognition system robust to illumination variations and moderate occlusion. Two main contributions are discussed. First, we introduce an approach based on Contrast Equalization (CE) to improve the traditional Weberface (WF) technique and make it more robust. Second, we use the Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) descriptors to make the Weberface method more resilient to extreme variations in illumination by exploiting both spatial-domain and frequency-domain information. Finally, by combining the outputs of the two descriptors, enhanced facial features are obtained which are shown to possess more discriminating power not only for variable lighting conditions but also for occlusion. Once the features are extracted, these are used with a simple Knearest neighbor classifier. The concept of using the (WF) model together with spatial and frequency domains descriptors is novel and proven to result in a robust system resilient to varying lighting conditions and small to moderate variations in pose, and moderate occlusion. The effectiveness of the method is validated and compared to many classical illumination compensation techniques over three public datasets; namely the Yale B, the extended Yale B, and the AR databases. The proposed algorithm is shown to consistently outperform existing techniques under different challenging environments.
  Keywords: face recognition; illumination normalization; local texture patterns; contrast enhancement; pattern classification.
  
  
• Geometric retrieval algorithm based ear biometry with occluded images  
  by Samik Chakraborty, Madhuchhanda Mitra, Saurabh Pal 
  Abstract: Ear is a potential biometric parameter which has drawn the attention due to its structural uniqueness and stability over the age, obesity, disease, expression etc. unlike other common biometric traits. In this work a geometric retrieval algorithm has been proposed for ear based biometric analysis with occluded image. First the occlusion problem is countered by an empirical data driven technique and then PSO based optimal features are extracted for comparison that reveals the authenticity of the subject with respect to a stored database. A search of minima from Euclidian distance based analysis is used for final decision. The proposed system is tested on 50 subjects collected in multiple sessions in laboratory with a good recognition rate superior to similar reported works as indicated in the result section.
  Keywords: biometry; ear recognition; occlusion; interpolation; particle swarm optimization; Euclidian distance.
  
  
• Eigen-based Binary Feature Amalgamation in Multimodal Biometrics  
  by Wen-Shiung Chen, Ren-He Jeng 
  Abstract: In this paper, a quantized eigen analysis (QEA) for the extracted features is proposed and rnan associated eigen-based binary feature amalgamation (EBFA) based on QEA is developed rnfor feature fusion in multimodal biometrics. rnAs opposed to feature combination, EBFA projects heterogeneous features onto the projection kernel and rnuses only the sign parts to encode the features as bit strings to maximize its expression rnrather than directly combine them. rnThus the feature codes can be simply concatenated or compared by XOR bit-wise operation rninto a serial or parallel amalgamated feature vector. rnTo evaluate the performance of EBFA, a series of experiments are performed on rnmultiple biometric modalities, including face, palm-print and iris. rnThe experimental results show that the proposed binary feature amalgamation scheme at feature-level rnis superior to some other feature fusion methods and score-level methods in terms of multimodal recognition accuracy performance.
  Keywords: Multimodal Biometrics; Feature-Level Fusion; Feature Combination; Feature Fusion; rnFeature Amalgamation; Eigen Analysis; Face; Iris; Palm-print.
  
  
• LAUGHTER SIGNATURE: A NOVEL BIOMETRIC TRAIT FOR PERSON IDENTIFICATION  
  by Comfort Folorunso, Olumuyiwa Asaolu, Oluwatoyin Popoola 
  Abstract: Laughter is a naturally occurring feature in speech and social interactions. Human intelligence can identify people by their laughter, but this has not been explored as a potential biometric in person identification systems. This study proposes a novel behavioral biometric based on individual laughter signatures. Mel Frequency Cepstral Coefficients (MFCC) features were extracted and Kruskal-Wallis test was performed on each coefficient. A Dynamic-Average Mel Frequency Cepstral Coefficients (DA-MFCC) was developed from the typical MFCC features for system training using Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). Test results showed an accuracy of 90%-person identification for SVM while the GMM was 65%. The use of GA-MFCC improved the accuracy of the system by 5.06% and 2.99% for GMM and SVM respectively. Laughter has thus been shown to be a viable biometric feature for person identification which can be embedded into artificial intelligence systems in diverse applications.
  Keywords: Person identification; laughter signature; biometrics trait; Support Vector Machine (SVM); Gaussian mixture Model(GMM).
  
  
• Efficient Fusion of Face and Palmprint in Gabor Filtered Wigner Domain  
  by Nirmala Saini, Aloka Sinha 
  Abstract: In this paper, a new transform Gabor Filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimization technique, particle swarm optimization (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.
  Keywords: Multimodal system; Feature level fusion; Score level fusion; Gabor filtered Wigner transform; Particle swarm optimization.
  
  
• Fingerprint pores extraction by using automatic scale selection  
  by Diwakar Agarwal, Atul Bansal 
  Abstract: Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilized multiscale ?-normalized Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average True Detection Rate (TDR) and average False Detection Rate (FDR). Results of the proposed algorithm achieve TDR and FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.
  Keywords: automatic scale selection;biometrics;fingerprint;local maxima;pores.
  
  
• Supervised and unsupervised machine learning for gender identification through hands anthropometric data  
  by Nahid Hida, Mohamed Abid, Faouzi Lakrad 
  Abstract: The goal of the present study is to determine the best gender identifiers from the hand anthropometric measurements. To do this, five methods for gender identification are used and their performances quantified. The first method is based on computing distances of test subjects to pre-computed masculine / feminine mean characteristics. The second method stands on determining the k-nearest neighbors of test subjects to known data. Furthermore, since the hand anthropometric measurements are showing high sexual dimorphism an unsupervised learning technique, the K-means algorithm, is applied and it is able to segregate males and females in two different clusters. Finally, the classical linear and quadratic discriminant techniques are also used. These five methods are applied to a database of hand anthropometric measurements of students from the authors department. The recursive feature elimination and the stepwise regression methods are used for selecting the relevant attributes for the gender identification process. The outcomes of these methods are leading to high accuracy rates of genders recognition. However, the linear and quadratic discriminant methods turned out to be the most accurate. Breadth and circumference features are revealed better than the length features in identifying the gender. The palm and the thumb are the parts of the hand with the highest rate of gender recognition. Furthermore, breadths of the index and the thumb and the palm circumference are the best individual identifiers.
  Keywords: Hand anthropometric data; supervised and unsupervised methods; gender identification; features selection.
  
  
• Sub-band based Feature Fusion and Hybrid Fusion Approaches for Multimodal Biometric Identification  
  by Rajeshwari Devi D V 
  Abstract: A Multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of Discrete Wavelet Transform (DWT), and Principal Component Analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through Linear Discriminant Analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least Equal Error Rate (EER) of 0.086 for ORL+CASIA database.
  Keywords: Multimodal biometrics; Feature level fusion; Sub-band fusion; DWT; PCA; LDA; Hybrid fusion.
  
  
• Ear recognition based on discriminant multi-resolution image representation  
  by Hakim Doghmane, Hocine Bourouba, Kamel Messaoudi, El Bay Bourennane 
  Abstract: The multi-resolution analysis is more appropriate for extracting information from measured data, because it is generally multi-scale in nature. This paper proposes a new approach for ear representation, based on multi-resolution analysis framework. Such representation relies on significantly Gabor wavelet, Local Phase Quantization (LPQ) descriptor and Spatial Pyramid Histogram (SPH) method. First, to capture the local structure in ear image, the Gabor wavelet function with two scales and four orientations is used. Second, to fully explore the blur invariant property and the texture information in different scales and directions spaces, the LPQ operator is applied on the image responses of Gabor filter to get label LPQ images. Third, the SPH of horizontal decomposition is applied for each of them, to obtain local ear feature descriptors. Next, the obtained histograms are normalized. Then, the global representation of ear image is obtained by concatenating all the local feature descriptors. After that, a discriminant representation of ear image is constructed using whitened linear discriminant analysis. Finally, the K-nearest neighbor classifier is used for identification. Experiments conducted on two ear databases (IIT Delhi-1 and IIT delhi-2); show that the proposed approach provides a significant accuracy improvement compared to the state of the art methods.
  Keywords: Ear biometric recognition; scale; local phase quantization; Gabor wavelet; spatial pyramid histogram; multi-resolution analysis.
  
  
• SUPER RESOLUTION AND RECOGNITION OF UNCONSTRAINED EAR IMAGE  
  by Anand Deshpande, Prashant Patavardhan 
  Abstract: In this paper, a framework is proposed to super-resolve low resolution ear images and to recognize these images, without external dataset. This frame uses linear kernel co-variance function based Gaussian Process Regression to super-resolve the ear images. The performance of the proposed framework is evaluated on UERC database by comparing and analyzing the peak signal to noise ratio, structural similarity index matrix and visual information fidelity in pixel domain. The results are compared with the state-of-the-art-algorithms. The results demonstrate that the proposed approach outperforms the state-of-the-art super resolution approaches.
  Keywords: Super resolution: ear recognition: Gaussian process regression: PSNR.
  
  
• A Comparison of Human Brainwaves-Based Biometric Authentication Systems  
  by Shikah Alsunaidi, Nazar Saqib, Khalid Alissa 
  Abstract: Several decades ago, attention was directed to biometrics as an alternative to passwords that can be discovered or "Shoulder Surfing" by others. Various authentication methods have been provided that rely on the user's biometrics, such as a fingerprint of a face, iris, voice, and others. Unfortunately, ways were found to imitate these visible fingerprints for using them in penetrations. Therefore, many researchers were interested in studying the possibility of using brainwaves for authentication purposes, as relying on hidden vital features increases the difficulty of breaking and imitation. This paper presents an analytical study of the proposed brainwave-based biometric authentication systems. It provides a comparison of signal acquisition methods for the brainwave-based authentication system. Also, the paper classifies brainwaves using its relevant features. It also presents the phases of the brainwave-based authentication system. Finally, it provides a detailed discussion of several factors that affect the accuracy of the brainwave-based authentication system results, and evaluate the compatibility level of the brainwave with the biometric requirements.
  Keywords: Authentication; continuous authentication; biometrics; brainwave; BCI; acquisition methods; intracortical; ECoG; EEG; MEG; fMRI; fNIRS.
  
  
• Investigating the Accuracy of Free-Text Keystroke Dynamics Authentication in Touchscreen Devices  
  by Suliman Alsuhibany, Muna Almushyti, Fatimah Alkhudhayr 
  Abstract: Security on smartphones has become a substantial topic since the amount of security relevant data and information stored on them has been increasing. While the password is commonly used as a traditional approach for user authentication, it suffers from a security-usability trade-off dilemma. Accordingly, the keystroke dynamics authentication approach can provide ease of use to the user as well as robust security. This paper investigates the feasibility of utilizing user-typing behaviors on touchscreen keypads for the authentication process via free-text keystroke dynamics. In constructing the timing vectors, three timing features are used: hold time, flight time, and Di-graph duration. Additionally, Euclidian and Manhattan distances were utilized to find the degree of similarity between the users log-in data and users profile. An Android application was developed and evaluated through an experimental study. Thus, we achieved a very encouraging result, as we were able to show that applying the free-text method indeed influences the accuracy of user authentication on a touchscreen device
  Keywords: Authentication; Security; Biometrics; Keystroke Dynamics; Touchscreen.
  
  

Special Issue on: Biometrics, Deep Learning and Sentiment Analysis

• Cascading Failure of Complex Networks under Degree-based Attack  
  by Yan Liu, Jie Yang, Peng Geng 
  Abstract: Frequent large-scale blackouts, network disconnections, and traffic jams have led to an increasing focus on the cascading failure of complex networks. This paper summarizes the research status of cascading failure in communication networks, power grids and independent networks. Considering the load and capacity of nodes, a cascading failure model of complex networks for degree-based attack is established. In this model, a new measurement method is proposed. This method is a normalized index to measure the impact of cascading failure, which is used to study the global consequences of different types of node failure. By attacking the nodes with higher and lower degree values, the effects of Higher-Degree-Based Attack (attacking nodes with a higher degree in descending order) strategy and Lower-Degree-Based Attack (attacking nodes with a lower degree in ascending order) strategy on network robustness are discussed. The simulations show that when certain conditions are met, attacking nodes with a lower degree (Lower-Degree-Based Attack) can cause greater damage. This result breaks the conventional thinking and provides a reference for the protection of important nodes in the real complex networks.
  Keywords: cascading failure; complex networks; degree-based attack.
  
  

Special Issue on: Recent Trends in Pattern Recognition and Biometrics

• Feature Recognition Method For Similar Key Points Of Human Face Based On Adaptive Median Filter  
  by Jing Liu 
  Abstract: In order to overcome the low efficiency and poor accuracy of current face feature recognition methods, this paper proposes a feature recognition method for similar key points of human face based on adaptive median filter. The preprocessing of face image including rotation, scaling and clipping was carried out to remove the influence of image background on feature recognition of similar key points of face. The gray level of the input image is mapped to the output image pixel by function mapping to achieve histogram homogenization of the gray face image.Based on adaptive median filtering, a face image denoising model is constructed and trained.The experimental results show that the recognition time of the proposed method is less than 0.7s, the SNR is higher than 24dB, the recognition accuracy is more than 90%, and the recognition effect is better.
  Keywords: Adaptive median filter; Similar key points of human face; Feature recognition; Image denoising.
  
  
• Research On Fast Identification Technology Of Forged Fingerprints Based On The Improved K-Means Algorithm  
  by Zhao-ting Ren 
  Abstract: In order to overcome the low accuracy of the traditional method, a fast identification method based on the improved k-mean algorithm is proposed. Spatial grid block model is constructed to extract the fingerprint texture features and then the fingerprint profile features are detected using the edge outline extraction method. The Kalman fusion method is used to reconstruct fingerprint information. Using the neighborhood distributed retrieval method, fingerprint image feature fusion is realized and the texture feature extraction model for forged fingerprints is established. The K-means clustering method is used for fingerprint feature clustering to realize fast identification of forged fingerprints. Experimental results show that the identification accuracy of this method is higher than 0.85, and the identification stability is good. The signal-to-noise ratio of fingerprint images is always between 25.3dB and 82.3dB, and the imaging quality is high, indicating that this method can realize fast and accurate identification of forged fingerprints.
  Keywords: K-means algorithm; forged fingerprint; fast identification; feature extraction; texture.
  
  
• Method for accurately identifying local fuzzy features of sprinting video images  
  by Zhiling Chen 
  Abstract: In order to improve the recognition ability of sprinter video image features, a method of image local fuzzy feature recognition based on edge contour feature matching was designed. Based on the model of image visual feature sampling, the spatial block region planning is carried out. The attitude determination model of the local fuzzy region is established, and the local fuzzy features are extracted by combining template matching and wavelet multi-scale decomposition. Block recognition and information enhancement technology are used to enhance the fuzzy region information so as to extract the edge contour feature set of the fuzzy region and realize the accurate recognition of the local fuzzy features of the image. The simulation results show that this method can accurately identify the local fuzzy features of sprint video images, and the highest recognition accuracy can reach 95.7%.
  Keywords: Sprinting; video image; local fuzzy feature; accurate identification; edge contour detection.
  
  
• Research On Fingerprint Feature Recognition Of Access Control Based On Deep Learning  
  by Xiaochang Lv, Li Ding, Guohua Zhang 
  Abstract: In order to overcome the problems of large error and long time-consuming in traditional feature recognition methods, this paper proposes a new fingerprint feature recognition method based on deep learning. Firstly, fingerprint identity database is established, and the access control fingerprint image is collected by the modified hardware equipment, and the image preprocessing is realized from two aspects: image screening and morphological processing. In this framework, the fingerprint direction field in the fingerprint image is screened through multiple iterations. The feature points in the fingerprint image of access control are extracted, and the similarity between the image and the information base is calculated. The experimental results show that compared with the traditional recognition method, the recognition speed of the proposed method is improved by about 6.6 seconds on the premise of ensuring the accuracy of recognition.
  Keywords: Deep learning; Access control fingerprint; Fingerprint feature; Feature recognition;.
  
  
• Research On Fingerprint Image Recognition Based On Convolution Neural Network  
  by Xin Zheng 
  Abstract: In order to overcome the problem of poor image matching performance of the image recognition method, a method of fingerprint image recognition based on convolution neural network is proposed. In this method, the defaced fingerprint image is preprocessed by smoothing, convergence, equalization, background foreground segmentation and distortion correction, and the feature points of the defaced fingerprint image are extracted by combining the neighborhood judgment method, and the information pseudo feature points are removed by fusing the feature points, the center points are extracted from the feature points of the defaced fingerprint image, and the center block image is identified by convolution neural network, so as to realize the defaced fingerprint image Distinguish. The experimental results show that the performance of restoration and reconstruction is improved. The rejection rate (FRR) is 3.75%, the false recognition rate (far) is 1.25%, and the correct recognition rate (CR) is 99.25%.
  Keywords: Convolution neural network; Defaced fingerprint; Image recognition; Neighborhood determination.
  
  
• Unconstrained Online Handwritten Uyghur Word Recognition Based On Recurrent Neural Network And Connectionist Temporal Classification  
  by Mayire Ibrayim, Wujiahemaiti Simayi, Askar Hamdulla 
  Abstract: This paper conducts the first experiments applying recurrent neural networks-RNN accompanied with Connectionist temporal classification-CTC to build end-to-end online Uyghur handwriting word recognition system. The traced pen-tip trajectory is fed to network without conducting segmentation and feature extraction. The network is trained to transcribe handwritten word trajectory to a string of characters in alphabet which has total 128 character forms. In order to avoid overfitting during training and improve generalization of the model, dropout technique is implemented. An online handwritten word dataset has been established and used for model training and evaluation in writer independent manner. Recognition results are evaluated by calculating the Levenshtein-edit distance and 14.73% character error rate CER on test set of 3600 samples for 900 word classes has been observed without help of any lexicon search and language model.
  Keywords: Online Handwriting Recognition; Recurrent Neural Networks; Connectionist Temporal Classification; Dropout; Uyghur Words.
  
  
• Research On Facial Feature-Based Gender Intelligent Recognition Based On The Adaboost Algorithm  
  by Jing Wang 
  Abstract: In order to overcome the problem of poor facial recognition intelligence and weak gender judgment, a new method based on Adaboost algorithm for facial feature-based gender intelligence recognition is proposed in this paper. In this method, the three-dimensional special point detection, weak perspective projection, spatial region segmentation and other methods are employed to construct the facial feature information sampling model. The Adaboost algorithm is used to analyze the matching between facial features and gender, on which facial-feature gender intelligent recognition is performed according to the the distribution of the eyes, nose and mouth of the face image, and the edge contour detection model of the face image is constructed. The experimental results show that the method has the advantages of good intelligence, high recognition precision and short time cost in face-based gender recognition.
  Keywords: Adaboost algorithm; facial features; gender; intelligent identification.
  
  

Special Issue on: Bio-Inspired Algorithms for Biometrics

• Face Spoofing Detection Using Improved SegNet Architecture with Blur Estimation Technique  
  by Sandeep Kumar, Sukhwinder Singh, Jagdish Kumar 
  Abstract: Biometrics has been increasingly used as the well-known technology for the identification and verification of a person. The huge demand of biometric in day by day life, cybercrime is going increase rapidly in the digital world. Among different biometric traits face has been extensively used for human identity and is therefore much vulnerable to face spoofing attacks. In this spoofing attack, Fake printed photo of user is presented in front of camera. In this proposed work, face liveness detection (FLD) scheme on photo attack using convolution neural network with texture-based blur estimation feature & elimination using Support Vector Machine (SVM). The face is detected with the help of improved SegNet based convolutional neural network (CNN) method. Blur measure on the basis of local min-max of left and right edges and Calculate blur of horizontal and vertical edges. Image filtering is done by adaptive median filter (AMF). The proposed & novel 5-Layer encoder decoder SegNet based algorithm improves the accuracy on various benchmark dataset i.e. NUAA, Replay, Printed, CASIA and live database for face liveness detection. The detection rate has reached up to 97% and time taken for liveness reduced up to one sec per image. This proposed algorithm shows better value of recall, precision and error rate as compared to earlier algorithms
  Keywords: Face Liveness; SVM; Blur Estimation; CNN; Adaptive Median Filter; Face Detection.
  
  

Special Issue on: Biometrics Challenges and Applications

• FACE DETECTION AND RECOGNITION SYSTEM BASED ON HYBRID STATISTICAL, MACHINE LEARNING AND NATURE BASED COMPUTING  
  by Vinodini Ramamurthy, Karnan M. 
  Abstract: Face detection becomes an important task carried out in biometric based security system and identification application. This paper presents the detailed investigations on different methods suffer from accuracy and computational complexity used for the face detection and recognition. The face detection and recognition with high performance ratio for face detection and recognition is achieved in the methods investigated. The reduction of complexity can happen at any stages of the face recognition like preprocessing, segmentation, feature extraction, recognition etc. The proposed method presented in this paper is based on PCA (principle component analysis), SVM (support vector machine), K-nearest neighbor (KNN) and ACO (ant colony optimization). The detail investigation of the proposed method is made and is compared with the existing methods. From the performance it can be observed that the proposed method is better in performance when compared to other methods.
  Keywords: Face detection; recognition; PCA; SVM; ACO; segmentation; feature extraction; classification.