International Journal of Biometrics (13 papers in press)
Forensic Dental Biometry A Human Identification System Using Panoramic Dental Radiographs Based on Shape of Mandibular Bone
by Mahroosh Banday, Ajaz Hussain Mir
Abstract: Dental Biometrics is a new and growing area of Forensic Biometrics that uses the unique features of dental structures from dental radiographs to automatically establish a persons identity from their dental remains when the conventional biometric features are not available. In this paper, we present a new and efficient approach for identifying people, by using the structure of mandible from the panoramic dental radiographs as a biometric identifier. The system automatically segments the mandible from dental panoramic images to extract the representative feature vectors for each mandible, which are later used for matching and identification. The experimental results of the proposed system using a database of 120 ante-mortem and 90 post-mortem panoramic dental images show that the system is robust and effective in identifying individuals and exhibits a high Recognition rate (RR) up to 98.79%, low Equal error rate (ERR) of 1.5% and a remarkable identification performance.
Keywords: Dental Biometrics; Odontology; Forensic identification; Mandible; Dental radiographs.
Fusion of Hand-shape and Palm-print Traits using Morphology for Bi-modal Biometric Authentication
by Wen-Shiung Chen, Wei-Chang Wang
Abstract: This paper presents a bimodal biometric recognition technique fusing hand-shape and palm-print traits of a human hand for personal authentication. In this fusion scheme, a novel feature extraction based on morphology, called broken mirror method, is designed and a two-stage recognition is proposed. We utilize the image morphology and concept of Voronoi diagram to slice the image of the front of the whole palm into several strips in which each strip is then decomposed into irregular blocks in accordance with the hand geometry. Furthermore, statistic characteristics of the gray level in each of the blocks is employed as characteristic values. In the final stage, a coarse recognition followed by a fine recognition will be adopted to recognize the identity. The experimental results show that the proposed biometric fusion system has an encouraging performance on recognition. The false acceptance rate (FAR) and false rejection rate (FRR) are reduced efficiently down to 0.0035% and 5.7692%, respectively. Our approach achieves the EER of about 7% which is better than that of other methods.
Keywords: Personal Authentication; Biometric Recognition; Multimodal Biometrics; Bimodal; Hand-shape; Palm-print; Morphology.
Face Detection cum Recognition system using novel techniques for Human Authentication
by Parivazhagan A, Brintha Therese A
Abstract: Face biometric plays a significant role in Human authentication system; today in several sectors to recognise a person Face biometrics are used. In accuracy point of view still, there is a lag in the perfect recognition system. In this work, novel ideas are proposed to develop a novel Face recognition, Face detection and Face detection cum recognition system. A novel Gray-averaging technique is combined with blooming Feature extraction techniques called Location averaging technique and Max-Min comparison technique for Face recognition and Face detection. An existing frequency domain process DCT is also joined with this system. In this system spatial domain and Frequency domain techniques are united, hence it acts as a bridge between these two techniques. The face detection cum recognition system is validated using parameters like Image size, Run time, and Accuracy with few face databases. This novel system is examined through five standard face databases and 300 real life face images.
Keywords: Face Recognition; Face Detection; Location Averaging Technique; Max-Min Comparison; Gray Averaging Technique; Discrete Cosine Transform; Feature Extraction; Human Authentication; Face Biometric; Face detection cum recognition.
Bidirectional aggregated features fusion from CNN for palmprint recognition
by Jianxin Zhang, Aoqi Yang, Mingli Zhang, Qiang Zhang
Abstract: In this paper, we present a novel bidirectional aggregated features representation from convolutional neural networks (CNN) with score-level fusion for palmprint recognition. Our method adopts the vector of locally aggregated descriptors (VLAD) to encode the convolutional features from two directions, i.e. vertical and horizontal directions, to mine both the local and global descriptions of palmprint image. Then, three score-level fusion rules are respectively employed to integrate the matching scores of the bidirectional features. We extensively evaluate the performance of convolutional features, vertical and horizontal encoding together with the score-level fusion rules through recent deep network VGG-F on the PolyU palmprint and multispectral palmprint databases. Experiments demonstrate that horizontal encoding significantly outperforms vertical encoding on red, green, blue and near-infrared (NIR) palmprint image subsets while it is slightly worse on PolyU palmprint database, moreover, the effective performance improvement can be achieved after the fusions.
Keywords: CNN; VLAD; bidirectional features; score-level fusion; palmprint recognition.
Fusing Iris and Periocular Recognition using Discrete Orthogonal Moment-based Invariant Feature-set
by Bineet Kaur, Sukhwinder Singh, Jagdish Kumar
Abstract: Iris recognition in uncontrolled environment poses a challenge due to occlusion noise, specular reflections and poor resolution. Therefore, periocular recognition has become a popular biometric modality which when used with iris recognition makes the system suitable for high-security applications. The paper introduces discrete orthogonal moment-based invariant features: Tchebichef, Krawtchouk and Dual-Hahn moments which provide discriminative features with compact information and minimum redundancy for non-ideal conditions. The proposed techniques are applied on two publicly available iris databases: IITD v1 and UBIRIS v2 and our own PEC, Chandigarh periocular database. Results demonstrate that the moment-based feature-set outperforms existing approaches available in the literature.
Keywords: Biometrics; Dual-Hahn Moments; Iris Recognition; Krawtchouk Moments; Orthogonal Moments; Periocular Recognition; Tchebichef Moments.
On the performance Improvement of Non-Cooperative Iris Biometrics using Segmentation and Feature Selection Techniques
by Alice Nithya, Lakshmi C
Abstract: In this work, an improved segmentation methodology and a novel statistical dependency based backward feature selection algorithm are proposed. From the input eye image, iris boundary is identified using Circular Hough Transform. A bounding box is defined using the radius obtained followed by iterative thresholding techniques to eliminate specular reflections, eyelids, eyelashes and pupil region. First-order and second-order statistical features are extracted from the segmented iris. For the first time, the statistical measure, Chi-Square value is computed from GLCM as a new novel feature from iris images. Statistical dependency based backward feature selection (SDBFS) algorithm is used to reduce the feature vector size. By operating on local features in reduced search space, computation complexity of segmentation is reduced with less mislocalization count and eliminates pupil dilation effects. Results of SDBFS show the usefulness of minimal-useful features. Experimental results conducted on CASIA V1, V3-Interval and UBIRIS V1 datasets show that statistical features in non-ideal iris images outperform some of the state-of-the-art methods.
Keywords: Iris Recognition; Circular Hough Transform; GLCM; Backward Feature Selection; Chi-Square Value; Segmentation; Statistical Dependency.
Human Age Classification using Appearance and Facial Skin Ageing Features with Multi-class Support Vector Machine
by Jayant Jagtap, Manesh Kokare
Abstract: Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with Multi-class Support Vector Machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle, and facial skin textural features extracted by using Local Gabor Binary Pattern Histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database, and achieved greatly improved age classification
accuracy of 94.45%.
Keywords: Appearance features; Facial skin ageing features;Multi-class Support Vector Machine (M-SVM); Local Gabor Binary Pattern Histogram (LGBPH); Wrinkle analysis; Age classification framework.
Bone- and Air-Conduction Speech Combination method for Speaker Recognition
by Satoru Tsuge, Shingo Kuroiwa
Abstract: In this paper, first, we report speaker recognition performance using
bone-conduction speech based on an i-vector-based speaker recognition system,
which is the current state-of-the-art method. In addition, we propose three speaker
recognition methods combining bone-conduction speech and air-conduction
speech: a feature combination method, a speaker model combination method,
and a similarity score combination method. To evaluate the proposed methods,
we conducted speaker recognition experiments using a part of a large speech
corpus constructed by the National Research Institute of Police Science, Japan.
Experimental results show the bone-conduction speech performs almost the
same as the air-conduction speech when the enrollment data and evaluation data
are collected in the same session. In addition, all proposed methods improved
the speaker recognition performance of air- and bone-conduction speech in the
experiments. From these results, we conclude that fusing air- and bone-conduction
speech improves the speaker recognition performance.
Keywords: Speaker recognition; Bone-conduction speech; Air-conduction speech; i-vector; Speech processing.
Biometric authentication system based on texture features of retinal images
by Jarina B. Mazumdar, S.R. Nirmala
Abstract: In biometric authentication system, distinct set of characteristic features are used to identify an authorised person. Retina is a stable biometric feature because of its location and unique physiological characteristics. In this paper, we propose a texture feature-based retinal authentication system. Texture features are considered as important features for authentication purpose. These texture features of retina are extracted using local configuration pattern (LCP) and Radon transform technique. The LCP computes the local structural information as well as the microscopic information of the image. Using Radon transform on retinal images, Radon features are extracted which contains the texture information of the blood vessels. A feature vector is formed by combining all theses LCP and Radon features and then fed to a feed-forward artificial neural network (FANN) classifier. This stage checks whether the test image belongs to the authorised person or not. Three general retinal databases DRIVE, HRF, Messidor, and images collected from two local eye hospitals are considered to authenticate a person. Two retinal authentication databases RIDB and VARIA are also used for evaluating the performance of the system. The results obtained show that the system is effective and efficient in authenticating the individuals.
Keywords: biometric; texture feature; local configuration pattern; radon transform; feed-forward artificial neural network; FANN; authentication; vessel pattern; classifier.
User attitude towards novel biometric system and usability analysis
by Ishan Bhardwaj, Narendra D. Londhe, Sunil K. Kopparapu
Abstract: The advent of biometrics as a mean of authentication for financial institutes, government agencies, and personal devices caused significant acceleration in the realisation of security solutions. Increasing deployments and affordable hardware components corroborate this statement. Though passwords have commendable user convenience but suffer from serious issues, which have evolved biometrics into highly secure and reliable authentication methods. Every biometric technique should be easy to learn, impel to use and user convenient. In this paper, we have proposed to analyse the usability of fingerprint dynamics by performing user preference-based experimentations. This is also followed by the study of various aspects of fingerprint dynamics as an authentication system. Results are reported for the usability trials which includes data collection from 348 participants, followed by comprehensive statistical analysis. User preferences were also measured using attitude questionnaires. As an indicator of system performance results of authentication experiments are also reported.
Keywords: fingerprint dynamics; biometrics; authentication; usability; user experience; user characteristics.
Towards contactless palm region extraction in complex environment
by Tingting Chai, Shenghui Wang, Dongmei Sun
Abstract: Palm region of interest (ROI) extraction is an indispensable procedure in palmprint recognition. Prior works generally perform well on palm ROI extraction because of dedicated devices and well-controlled environment. To make hand placement less-constrained and improve usability, mobile palmprint recognition has attracted a wide attention in recent years. For mobile phone images captured in complex natural environment, palm ROI extraction is a challenging work due to varying illumination, complex background and contactless acquisition mode. In this paper, a mobile palmprint dataset (SPIC) is at first established with five smartphones, comprising 4000 images collected from 128 persons in two separate sessions. Furthermore, a novel pre-processing approach is proposed to achieve ROI extraction in mobile scenarios, which include colour component selection, learning-based fast hand segmentation and geometry-driven valley point location. Experimental results demonstrate that the proposed method can achieve high extraction accuracy and computational efficiency on PolyU1.0, HA-BJTU and SPIC palmprint databases.
Keywords: palm ROI extraction; palmprint recognition; hand segmentation; contour tracking; valley point detection.
Low and high frequency wavelet sub-band-based feature extraction
by D.V. Rajeshwari Devi, K. Narasimha Rao
Abstract: In a biometric system, feature extraction is an important task for faster and efficient identification of a person. A new feature extraction method, sub-band PCA+LDA is proposed to extract distinct features from low frequency and high frequency wavelet sub-bands. The proposed method captures both local and global features of two biometrics under consideration, face and iris. The matching scores of face and iris are normalised using minmax and tanh techniques, and fused using sum rule, product rule and weighted sum rule. For unimodal systems, the proposed method gives better recognition rate in comparison to other existing methods, like DWT, DWT+PCA, DWT+LDA, local binary pattern and subspace LDA. The performance of the proposed multimodal biometric system is superior to unimodal system in terms of attaining maximum of 100% recognition rate and minimum equal error rate (EER) of 0.017 for standard biometric databases.
Keywords: multimodal biometrics; sub-band fusion; feature extraction; discrete wavelet transform; principal component analysis; linear discriminant analysis; matching score level fusion.
Offline signature verification using shape correspondence
by Pradeep N. Narwade, Rajendra R. Sawant, Sanjiv V. Bonde
Abstract: Biometrics has always been an integral part of human identification and verification, with offline signature verification being a most crucial component of it. It is a challenging task as the signatures are time variant. To address the above difficulty, this paper presents a novel approach to identify the correspondence between pixels of different signatures using an adaptive weighted combination of shape context distance and Euclidean distance. These correspondences are then used for the transformation of query signature plane to reference signature plane using thin plate spline transformation. The distances between signatures are computed using plane transformation, a shape descriptor, and the farness between matched pixels. The computed distances are then fed to the support vector machine (SVM) classifier to determine the merit of genuineness. With the proposed methodology, better accuracy is obtained. The results exhibit an accuracy of 89.58% using proposed method on GPDS synthetic signature database.
Keywords: handwritten signature verification; pattern recognition; pattern analysis; shape matching; thin plate spline transformation; shape context; document analysis.