International Journal of Biometrics (19 papers in press)

Regular Issues

• Latent fingerprint segmentation using multi scale attention U-net  
  by AKHILA P, Shashidhar G. Koolagudi 
  Abstract: Latent fingerprints are the fingerprints lifted from the crime scene surfaces. Segmentation of latent fingerprints from the background is an important preprocessing task which is challenging due to the poor quality of the fingerprints. Though fingerprint segmentation approaches based on their orientation and frequency are reported in the literature, they could not adequately address the problem. We propose a latent fingerprint segmentation model based on the U-Net attention network in this work. We added the Atrous Spatial Pyramid Pooling (ASPP) layer to the network to facilitate multi-scale fingerprint segmentation. Our approach could effectively segment the latent fingerprint region from the background and even detect occluded and partial fingerprints with simple network architecture. To evaluate the performance, we have compared our results with the manual ground truth using NIST SD27A dataset. Our segmentation model has improved matching accuracy on the NIST SD27A dataset.
  Keywords: latent fingerprint segmentation; U-Net; attention; weighted cross entropy; multi-scale.
  DOI: 10.1504/IJBM.2024.10056003
   
  
• A Unique Approach Towards Keystroke Dynamics Based Entry-point User Access Control  
  by Soumen Roy, Devadatta Sinha, Rajat Pal, Utpal Roy 
  Abstract: Access control is an essential security service for computing devices, applications, and information. Among the different entry-point user access controls, keystroke dynamics (KDs) has gained popularity owing to its several merits, such as low cost, ease of usage, etc. In this study, we proposed a unique distance-based anomaly detector together with an appropriate template construction method leading to more realistic and accurate results. We validated our approach with ten standard datasets and compared the performance with 50 state-of-the-art anomaly detectors. In our consideration, recent anomaly detectors have been re-evaluated in the same experimental setting for sound comparison. An analysis of variance (ANOVA) was conducted to compare the performance of our approach to those detectors in both desktops and recent smartphones. This study provides an in-depth understanding of each detector’s performance which will aid in the design of efficient KD-based access control in the next generation of smart devices and applications.
  Keywords: access control; anomaly detection; keystroke dynamics; static authentication; template formation; template adaptation; touch dynamics; user authentication.
  DOI: 10.1504/IJBM.2024.10056899
   
  
• Fingerprint Multiple-Class Classifier: Performance Evaluation on Known and Unknown Fingerprint Spoofing Materials  
  by DIVINE AMETEFE, Suzi S. Sarnin, Darmawaty M. Ali, Dah John, Abdulmalik A. Aliu 
  Abstract: Fingerprint recognition is a popular and reliable biometric technology used in many security-sensitive applications. However, the use of fake fingerprints made from ubiquitous spoofing materials poses a significant threat to security systems. While several studies have proposed binary classifiers to detect fingerprint presentation attacks, relatively none have explored the effectiveness of multiple-class classifiers in detecting known and unknown spoofs. In this study, we evaluated the efficacy of multiple-class classifiers using deep transfer learning to detect presentation attacks made with different spoofing materials. Our experiments on the LivDet 2009 to 2015 datasets showed that while a classifier model developed without data augmentation performed better on known spoofs, it showed poor performance on cross-material detection of all seven fingerprint spoofing materials. These results suggest that modelling a multiple-class classifier is not an efficient approach for detecting cross-material presentation attacks in fingerprint recognition systems.
  Keywords: fingerprint spoofing; multiple-class classifier; known spoofing materials; unknown spoofing materials; deep transfer learning.
  DOI: 10.1504/IJBM.2024.10057333
   
  
• A comparative study on friction ridge pore features of males and females  
  by Anjana CD, Priyatha CV, Siva Prasad MS 
  Abstract: The sweat pores on the epidermal ridges of fingertips are unique and they are employed in personal identification. This study aimed to observe and analyse the pores within the left thumbprints of 50 individuals to find out whether there were any sex-related changes in the features of the pores. There was a significant (p < 0.05) difference in the average number of closed pores in males (53.60 ± 40.52) than that in females (81.60 ± 38.43). The average number of pores per 25 mm square was more frequent in females (156.12 ± 68.41) than males (105.12 ± 77.47). The difference in the distribution of pores per 5 mm of ridge length was found significant (p < 0.05) between males and females. The third-level features like type, shape, and frequency of the pores of males and females can be used as a presumptive indicator to determine the sex from fingerprints.
  Keywords: fingerprints; poroscopy; AFRS; personal identification; sex difference.
  DOI: 10.1504/IJBM.2024.10057334
   
  
• A Minutiae-Based Method to Store and Compare Fingerprints  
  by Eiman Alhamad, Mohammed Al Logmani, Abdullah Essa, Mohammad Hammoudeh 
  Abstract: Biometrics refers to certain physical or behavioural characteristics that are unique to every person. Biometrics, including fingerprints, are used for the measurement and analysis of biological data for identification purposes. This paper presents a new method to extract and compare fingerprint biometrics based on minutiae features. Only two reference minutiae are used to enhance the efficiency of the verification process with no need to match all the combinations of the extracted minutiae from the intellectual-reader with the reference minutiae in the alignment algorithm. The method is implemented and tested with an average decrease of 80% in the number of combinations required to be matched with the reference minutiae when two reference minutiae points are used instead of one to align and match fingerprints.
  Keywords: biometrics; authentication; fingerprint; minutiae.
  DOI: 10.1504/IJBM.2024.10059360
   
  
• Tennis players’ hitting action recognition method based on multimodal data  
  by Song Liu 
  Abstract: In order to improve the recognition accuracy of hitting movements, a tennis player hitting movement recognition method based on multimodal data is proposed. Collect acceleration modal data of hitting movements and extract acceleration characteristics of hitting movements; collect deep modal data of hitting movements and extract deep optical flow features of hitting movements; collect RGB modal images of hitting movements, and use recurrent neural networks to extract RGB features of hitting movements. The canonical correlation analysis method is selected to fuse the acceleration characteristics, depth optical flow characteristics and RGB characteristics of tennis players’ hitting movements. The feature fusion result is taken as the input of the spatiotemporal convolutional neural network, and the spatiotemporal convolutional neural network is used to output the tennis player’s stroke action recognition result. The experimental results show that this method effectively recognises tennis players’ hitting movements, with an accuracy of over 99%.
  Keywords: multimodal data; tennis players; stroke action; recognition method; acceleration; deep optical flow characteristics.
  DOI: 10.1504/IJBM.2024.10059890
   
  
• A Sparse Representation Based Local Occlusion Recognition Method for Athlete Expressions  
  by Shaowu Huang 
  Abstract: A sparse representation-based local occlusion recognition method for athlete expressions is proposed to address the problems of large mean square error, low recall rate, and poor recognition performance. Calculate the gradient direction and size of image pixels, divide image blocks, count the histogram of the gradient direction of the image blocks, combine all small histograms into a feature vector, and obtain the facial feature extraction results; the LBP algorithm is used for local occlusion image segmentation, and a sparse representation model is established to extract expression features. By dividing the image into blocks and solving the sparse representation coefficients of each block, local occlusion expression recognition is achieved. Experimental results show that the maximum mean squared error of the proposed method for facial expression recognition is only 0.21, and the maximum recall rate is more than 80%, which shows that it can effectively recognise occluded parts.
  Keywords: sparse representation; localised occlusion of facial expressions; HOG; LBP algorithm; feature extraction.
  DOI: 10.1504/IJBM.2024.10059891
   
  
• Recognition of Starting Movement Correction for Long Distance Runners Based on Human Key Point Detection  
  by Xia Zhu  
  Abstract: In order to improve the accuracy and effectiveness of recognition of starting motion correction for long-distance runners, a method for recognising starting motion correction for long-distance runners based on human key point detection is proposed. Adopting sparse sampling method to process and collect starting action data, introducing STI module to extract data features and fuse them. Construct a human key point detection network based on collaborative spatiotemporal attention, collect dynamic gradient information of the input data, and use the collaborative spatiotemporal attention module to obtain all joint point information to complete the recognition of starting movements of long-distance runners. The results show that the proposed method has a recognition accuracy of over 96%, a root mean square error of always 0.01, and a recognition time of 1.8 seconds, indicating that the proposed method can achieve correction and recognition of starting movements of long-distance runners.
  Keywords: key points of the human body; starting movement; corrective identification; STI module; convolutional algorithm for central difference graph.
  DOI: 10.1504/IJBM.2024.10059892
   
  
• Speech endpoint detection method based on logarithmic energy entropy product of adaptive sub-bands in low signal-to-noise ratio environments  
  by MingHui Zhu, PengCheng Huang, JiaYong Zhang 
  Abstract: In this paper, a detection method based on logarithmic energy entropy product of adaptive sub-bands is designed. After the speech signal is divided into frames and FFT, the probability of the existence of the speech signal is analysed according to the ratio of the minimum value of the local energy spectrum to the short-term energy spectrum. After the noise is suppressed according to the normal distribution of Gaussian noise, the logarithmic energy entropy product of adaptive sub-bands is calculated. Using the calculated results as a threshold, compare the logarithmic energy spectral ratio of the current speech frame with the threshold, and use Bayesian classification to detect speech endpoints. Experiment shows that the detection accuracy of this method is always higher than 94.4%, and the accuracy variance is between 0.055 and 0.072, effectively achieving the design expectations.
  Keywords: voice signal; signal-to-noise ratio; SNR; voice endpoint; short time energy spectrum value; denoising; sub-bands logarithmic energy entropy product; accuracy.
  DOI: 10.1504/IJBM.2024.10059893
   
  
• Offline handwritten signature recognition based on generative adversarial networks  
  by Xiaoguang Jiang 
  Abstract: In order to shorten the time for offline handwritten signature recognition and reduce the probability of false positives, an offline handwritten signature recognition method based on generative adversarial networks is proposed. Firstly, select pen pressure, pen tilt angle, pen azimuth angle, and multi-level velocity moment as the main dynamic features of offline handwritten signatures, and calculate the Pearson correlation coefficients of these dynamic features. Secondly, calculate and sum multiple features to complete the dynamic feature selection and fusion of offline handwritten signatures. Finally, using dynamic feature fusion data as input and offline handwritten signature recognition results as output, a generative adversarial network model is constructed to complete the recognition of offline handwritten signatures. Experimental results show that this method can complete the recognition of 200 offline handwritten signatures in 0.66 seconds, with an error rejection rate and error acceptance rate of only 1%, and a recognition accuracy rate of 95%.
  Keywords: dynamic features; offline handwriting; signature recognition; Pearson coefficient; adversarial neural network; convolutional neural network; CNN.
  DOI: 10.1504/IJBM.2024.10059894
   
  
• A Method for Recognizing Wrong Actions of Martial Arts Athletes Based on Keyframe Extraction  
  by Zhiqiang Li 
  Abstract: In order to improve the accuracy of incorrect action recognition and shorten the time required for action recognition, the paper proposes a method for recognising incorrect actions of martial arts athletes based on keyframe extraction. Firstly, the optical flow method is used to filter the key frames of actions, and the shot adaptive K-means clustering algorithm is used to extract the texture features of image frames. Secondly, use Euclidean distance to calculate the distance between cluster centres and complete the initial selection of keyframes. Finally, optimise the sequence position and video frame rate of the initially selected keyframes to obtain the final keyframe sequence number and output the error action recognition result. The experimental results show that the error action recognition accuracy of this method is 96.58%, the recognition error is 1.9%, and the recognition time is 11 seconds.
  Keywords: keyframe extraction; optical flow method; Lens adaptation; K-means clustering algorithm; Euclidean distance.
  DOI: 10.1504/IJBM.2024.10059895
   
  
• Classification of Visual Attention by Microsaccades using Machine Learning  
  by Soichiro Yokoo, Nobuyuki Nishiuchi, Kimihiro YAMANAKA 
  Abstract: This paper proposes machine learning methods for classifying visual attention. Eye-tracking data contains a range of useful information related to human visual behaviour. In particular, many recent studies have shown a relationship between visual attention and microsaccades, a type of fixational eye movement. In this study, eye movement and pupil diameter were measured under three controlled experimental conditions requiring different visual attention levels. Microsaccades were extracted from eye-tracking data that included rapid saccades. Various machine learning methods were then used on parameters related to the extracted microsaccades to classify the level of visual attention. By cross- validating data from one participant (test data) with that from other participants (training data), we showed that the support vector machine method had the highest correct discrimination rate (77.1%). These results suggest that it is possible to classify visual attention based on microsaccades.
  Keywords: Microsaccade; Machine Learning; Visual Attention; Pupil Diameter; Eye-tracking.
  DOI: 10.1504/IJBM.2024.10060309
   
  
• An Online Learning Behavior Recognition Method Based on Tag Set Correlation Learning  
  by Ruijing Ma 
  Abstract: Aiming at the problems of poor fitting degree of loss function and low confidence of behaviour recognition in online learning behaviour recognition, an online learning behaviour recognition method based on tag set correlation learning is proposed. Firstly, analyse learners’ online learning behaviour and extract their online learning behaviour data through convolutional layer models; then, Gaussian mixture model is used to extract feature data, and EM algorithm is used to preprocess feature data; Finally, the label set correlation learning method is used to obtain the label rating results of each behaviour data, and normalisation processing is performed to identify and judge its correlation with the behaviour sample, completing the final recognition. The results show that the loss function value of the proposed method approaches 0, has a high fitting degree, and the confidence is 98%, and the recognition effect is better.
  Keywords: online learning; learning behaviour recognition; Gaussian mixture model; EM algorithm; label set correlation learning.
  DOI: 10.1504/IJBM.2024.10060536
   
  
• Chinese Named Entity Recognition Method Based on Multiscale Feature Fusion  
  by Xiaoguang Jiang 
  Abstract: In response to the problems of low recognition accuracy and poor recognition efficiency in traditional methods, the paper proposes a Chinese named entity recognition method based on multiscale feature fusion. Firstly, the similarity between each word is calculated using the literal similarity algorithm to obtain synonyms of Chinese named entities. Then, the Chinese named entity features are obtained, including character features, character shape features, binary character features, and word similarity features, through multiscale feature fusion to obtain the Chinese named entity feature set. Finally, the target Chinese named entity for recognition is obtained by matching vocabulary, compressing vocabulary vectors, and integrating character vectors, and the CRF is used to implement Chinese named entity recognition. The experimental results show that the recognition time of this method is only 4.0 s, with a precision rate of up to 99.9% and a recall rate of up to 99.2%.
  Keywords: multiscale feature fusion; similarity; CRF; literal similarity algorithm.
  DOI: 10.1504/IJBM.2024.10060537
   
  
• Accurate facial expression recognition method based on perceptual hash algorithm  
  by Yang Yang 
  Abstract: To improve recognition accuracy, a precise facial expression recognition method based on perceptual hash algorithm is proposed. Firstly, the single scale Retinex algorithm is used to enhance facial expression images. The image is divided into high-frequency and low-frequency parts through curvature change decomposition, and the image is enhanced after filtering processing. Secondly, the two-dimensional principal component analysis network is combined with a perceptual hash algorithm based on a simplified Watson model to extract image features. Finally, the feature is added to the Hash table, and based on the distance between the feature and the facial expression to be recognised, the nearest neighbour of the facial expression to be recognised is judged to achieve accurate facial expression recognition. The experimental results show that the face recognition accuracy of this method reaches over 95%, indicating that its recognition effect is good.
  Keywords: perceptual hash algorithm; facial expression recognition; image enhancement; feature extraction; hashtable.
  DOI: 10.1504/IJBM.2024.10060538
   
  
• Rapid Recognition of Athlete's Anxiety emotion Based on Multimodal Fusion  
  by Li Wang  
  Abstract: The diversity of anxiety emotions and individual differences among different athletes have increased the difficulty of emotion recognition. To address this, a rapid recognition method of athlete’s anxiety emotion based on multimodal fusion is proposed. Wireless sensor networks are used to collect facial expression images of athletes, and wavelet transform is applied for denoising the collected images. Image features are extracted using grey-level co-occurrence matrix, and the athlete’s facial expression images are normalised. Features related to the athlete’s emotions, such as voice characteristics, facial expression features, and physiological indicators, are obtained. These features from different perceptual modalities are fused to achieve rapid recognition of athletes’ anxiety emotions. The test results demonstrate that this method not only improves the image denoising effect but also achieves high accuracy and efficiency in emotion recognition, enabling accurate and real-time recognition of athletes’ emotions.
  Keywords: multimodal fusion; rapid recognition; wireless sensor networks; wavelet transform.
  DOI: 10.1504/IJBM.2024.10060859
   
  
• Facial micro-expression recognition method based on CNN and Transformer mixed model  
  by Yi Tang, Jiaojun Yi, Feigang Tan 
  Abstract: The existing methods for facial microexpression recognition have the problem of low efficiency and accuracy. Therefore, a facial micro-expression recognition method based on a hybrid model of CNN and transformer is proposed. Extract facial hierarchical features using a hybrid model of CNN and transformer, and use them as inputs to a deep network. At the same time, the facial microexpression image area is segmented and the image is smoothed through threshold to obtain the feature vectors of the facial microexpression. These feature vectors are input into a CNN and transformer hybrid model to achieve recognition of facial microexpressions. The experimental results show that the proposed method can recognise facial microexpressions in complete or incomplete images, and the recognition state delay is controlled below 5 ms. In addition, compared to traditional methods, this method has a higher average recognition accuracy, up to 98%.
  Keywords: CNN; transformer mixed model; micro-expression of human face; recognition method.
  DOI: 10.1504/IJBM.2024.10060860
   
  
• Multi-modal human motion recognition based on Behaviour tree  
  by Qin Yang, Zhenhua Zhou 
  Abstract: Since the efficiency and accuracy of existing methods are low in complex multi-modal human motion recognition, this paper studies the multi-modal human motion recognition method based on behaviour tree. Firstly, Kinect sensor is used to collect multi-modal motion data of human body, and convolutional neural network is used to denoise the collected motion data. On the basis of denoising data, wavelet packet decomposition is used to extract its features. Finally, according to the extracted multi-modal human motion features, a behaviour tree model is constructed to traverse the recognised human motion and achieve accurate and efficient multi-modal human motion recognition according to the degree of feature matching. The experimental results show that the recognition accuracy of the proposed method can reach 98%, the highest recall rate is 96%, the highest F1 is 0.97, and the longest recognition time is only 4.65 seconds, which indicates that the proposed method has high practicability
  Keywords: Kinect sensor; Behaviour tree; Convolutional neural network; Multimodal human motion recognition.
  DOI: 10.1504/IJBM.2024.10060861
   
  
• Identifying Illegal Actions method of Basketball Players Based on Improved Genetic Algorithm  
  by Zhenyu Zhu 
  Abstract: In order to reduce the time required for identifying athlete violations and improve the recognition rate, this paper proposes a basketball player violation recognition method based on an improved genetic algorithm. Firstly, the surface electromyographic signals of athletes are collected using a wireless sEMG signal acquisition device; Secondly, determine the location of signal acquisition and extract the time-domain features of the signal; Then, a composite filter is used to denoise the signal; Finally, the genetic algorithm is improved by combining support vector machines to design an action recognition classifier, which outputs the results of illegal action recognition through the recognition classifier. Through experiments, it can be seen that this method can effectively improve the recognition rate by 9.44%, and within 0.5 minutes, the recognition effect of basketball players' illegal actions is good.
  Keywords: Improved genetic algorithm; Wavelet transform threshold denoising; Action recognition classifier; Surface electromyographic signal.
  DOI: 10.1504/IJBM.2024.10060862