International Journal of Biometrics (17 papers in press)

Regular Issues

• Enabling secure authentication using fingerprint and visual cryptography  
  by Sneha Manohar Annappanavar, Pallavi Vijay Chavan  
  Abstract: Biometrics is the form of information associated with an individual that helps in unique identification and verification at different platforms. The fingerprint is an important biometric information and has become most popular for authentication and authorisation. However, maintaining the secrecy of fingerprint data becomes a challenging task over the cloud. This paper presents a novel encryption approach using visual cryptography that encrypts fingerprints and stores them in the form of shares. The visual cryptography scheme implemented in this paper is expansionless and has no greying effect. The authors have collected fingerprint data from residential societies. The algorithm achieves 100% of the peak signal-to-noise ratio. This ratio is highest when compared with state-of-the-art methods of visual cryptography schemes. The mean square error achieved is zero which helps in 100% correct identification of the fingerprint. The paper also presents a secure voting mechanism using fingerprint authentication for general elections.
  Keywords: biometrics; fingerprint recognition; confidentiality; authentication; visual cryptography; secret sharing; shares.
  DOI: 10.1504/IJBM.2024.10063996
   
  
• Enhancing security and accuracy in biometric systems through the fusion of fingerprint and gait recognition technologies  
  by Mayank Shekhar, Amit Kumar Trivedi, Ripon Patgiri 
  Abstract: In the evolving landscape of security technology, biometric systems are pivotal for unique identification through physiological or behavioural traits. This research focuses on enhancing biometric system security and accuracy by integrating fingerprint and gait recognition technologies. Fingerprint recognition is valued for its precision and ease of data acquisition, while gait recognition offers non-invasiveness and resistance to obfuscation. The study explores feature and score level fusions of these modalities, utilising advanced algorithms to optimise the integration and elevate recognition performance. Experimental evaluations demonstrate that the proposed multimodal system not only outperforms unimodal systems but also strengthens robustness against spoofing attacks. Key contributions include a novel gait feature extraction technique compatible with fingerprint features and an optimised score-level fusion algorithm, significantly enhancing accuracy and security. Biometric security systems have become integral to modern security architectures, leveraging unique physiological and behavioural characteristics to authenticate individuals.
  Keywords: multimodal biometrics; fingerprint recognition; gait recognition; biometric security; feature fusion; biometric authentication.
  DOI: 10.1504/IJBM.2025.10068285
   
  
• Facial thermograms - application of facial recognition in medical sector  
  by Swagata Sarkar, R. Muthuselvan , N. Ashokkumar , Rajesh Kumar Vishwakarma 
  Abstract: Millions of people around the world have recurrent migraines, which are a nerve disease that can be very bad. This study finds a big difference in temperature in the frontal and temporal areas of the right brains of women who had headaches on one side only. Notably, the temperature trends of people who had pain on both sides did not change, which suggests that the diagnostic process may be more complex. More study with bigger groups is still required. Face thermography should still be read with care, though, because more research and proof are needed. Facial thermography has a lot of potential to help doctors diagnose headaches better and learn more about how they work on a neurophysiological level. The system was trained with 1980 images and then tried with 576 images. It got a score of 96.66% for accuracy.
  Keywords: female; headache; humans; migraine disorders; quality of life; pain; temperature; thermography.
  DOI: 10.1504/IJBM.2025.10069534
   
  
• Classification of human emotion using an EEG-based brain-machine interface: a machine learning approach  
  by Abdul Cader Mohamed Nafrees, Sidath Ravindra Liyanage, Naomal G.J. Dias 
  Abstract: The main purpose of this work is to investigate the possibility of using electroencephalography (EEG) data to improve machine learning models’ ability to accurately identify emotions. The work focuses on emotion classification using EMG data, to improve data mining models. This work investigates the use of individual and ensemble classification methods in the processing of windowed data obtained from four scalp sites. This information is then utilised to calculate the emotions that participants felt at particular times. The results indicate that the use of a low resolution, readily available EEG device can be a useful tool for determining a human’s emotional status. The submission of ensembling technique increase the accuracy of the model, this highlights the possibility of creating categorization algorithms that may be used in practical decision support systems. Future studies in this field ought to concentrate on determining if the method, attribute creation, attribute selection, or both were responsible for this notable improvement.
  Keywords: electroencephalography; EEG; electromyography; EMG; facial expressions; human emotion; machine learning; ML.
  DOI: 10.1504/IJBM.2025.10070061
   
  
• Feature ranking for effective continuous user authentication using keystroke and mouse dynamics with the cat recurrent neural model  
  by Princy Ann Thomas, Preetha Mathew Keerikkattil 
  Abstract: Behavioural biometric modalities such as keystroke and mouse dynamics are ideal for continuous user authentication due to their non-intrusive quality. The success of the authentication framework is largely determined by the discriminative power of the features used. It is critical to be able to select the necessary discriminative features for optimal authentication performance. In this research, we implement multiple ranking algorithms on features derived from temporal information of keystroke and mouse dynamics to distinguish their discriminative capacity. The ranked features are then employed for continuous authentication using the cat recurrent neural model (CRNM) to optimise the search space and authenticate users. The experimental results given in this work propose a strategy for developing commercially deployable continuous authentication systems with broad applicability. Experiments are carried out with filter, wrapper, and embedded feature ranking approaches, and authentication outcomes are compared with the CRNM framework. The findings indicate that discrimination is manifested in uncommon rather than normal user conduct. Furthermore, it is discovered that applying feature ranking reduces authentication time from 198 seconds to 138 seconds and improves accuracy from 98.25% to 99.21%.
  Keywords: ranking; temporal features; keystroke dynamics; mouse dynamics; cat swarm optimisation; recurrent neural model.
  DOI: 10.1504/IJBM.2024.10064403
   
  
• A systematic literature review and bibliometric analysis of signature verification spanning four decades  
  by Sameera Khan, Dileep Kumar Singh 
  Abstract: This article conducts a systematic literature review and bibliometric analysis spanning four decades of research in the field of signature verification (SV). SV holds substantial significance in practical domains like finance, law enforcement, and document authentication. The primary objective of this study is to offer a comprehensive overview of SV's evolution, pinpoint research trends, and illuminate gaps within the existing literature. The review encompasses 1,552 studies published from 1982 to the present, with analysis focusing on various SV facets such as feature extraction, classification algorithms, datasets, evaluation metrics, and applications. The findings underscore substantial growth and diversification within the field, showcasing the development and testing of diverse approaches. Nevertheless, challenges such as the absence of standardised evaluation metrics and limited accessibility to public datasets emerge. The article concludes with a discourse on prospective directions for SV, considering the potential influence of emerging technologies like deep learning and biometric authentication on the field's future.
  Keywords: signature verification; SV; bibliometric analysis; thematic evaluation; cluster analysis.
  DOI: 10.1504/IJBM.2025.10064620
   
  
• An action recognition of track and field athletes based on Gaussian mixture model  
  by Qin Yang, Zhenhua Zhou 
  Abstract: To solve the problem of low recognition accuracy caused by the complexity of individual actions in track and field in the past, a method of action recognition for track and field athletes based on Gaussian mixture model was proposed. First, the data is analysed by the interaction of spatiotemporal features. Secondly, a low-pass filter is used to eliminate the impact of noise on the data to reduce the calculation loss. On the basis of pre-processing data, Hilbert Huang transform (HHT) was used for feature extraction to capture and understand athletes' motion features more accurately, thus significantly improving the accuracy of movement recognition. Then, the Gaussian mixture model is used to model the characteristic parameters, determine the number of mixed components and initialise the model parameters, and complete the movement recognition of track and field athletes. The experimental results show that the traditional method has high computational loss and low recognition accuracy, while the proposed method has very low computational loss and the highest recognition accuracy can reach 98%. The comparison shows that this method has the advantages of low computational complexity, high accuracy and good recognition performance.
  Keywords: Gaussian mixture model; GMM; interaction of spatiotemporal features; action data; low pass filter; athlete movement recognition.
  DOI: 10.1504/IJBM.2025.10064813
   
  
• Symbolic data analysis-based few-shot learning for offline handwritten signature verification  
  by Mohamed Anis Djoudjai, Youcef Chibani, Adel Hafiane 
  Abstract: This paper presents a novel approach for offline handwritten signature verification using few-shot learning and symbolic data analysis. The method effectively handles high intra-class variability and limited data availability, common challenges in signature recognition. The model is trained on dissimilarities from the Signet feature extractor, capturing subtle differences within the same writer's signatures. A new weighted membership function measures similarity between query and reference signatures. The method outperforms traditional approaches, achieving competitive equal error rates on four public datasets (GPDS, CEDAR, MCYT, PUC-PR) using only five genuine reference signatures. The system surpasses state-of-the-art results on GPDS and PUC-PR datasets. This combination of few-shot learning and symbolic data analysis offers robust and efficient signature verification, ideal for real-world applications with scarce labelled data.
  Keywords: few-shot learning; FSL; signature verification; intra-class variability; one-class symbolic data analysis classifier; dissimilarities.
  DOI: 10.1504/IJBM.2025.10065178
   
  
• A discriminative model for scale, translation and rotation invariant face recognition  
  by Puja S. Prasad, Esther Varma, Sanjay Kumar Prasad 
  Abstract: There are many challenges like different illumination conditions, ageing, different poses and orientation of images, limited datasets for training, and other variational conditions associated with facial recognition and verification. SIFT is a robust and popular algorithm for facial recognition due to its invariant nature towards scale, and orientation, but it has its some limitations. This paper proposes a framework in which we modify the steps of SIFT algorithm in two ways. First, for calculating extrema using a non-maximal suppression algorithm we compare the grid in fixed patches instead of whole images, and by reducing the size of the SIFT feature descriptor. For this experiment, we are using five public databases FERET, Yale2B, M2VTS, Face 94, ORL and found an improvement in terms of accuracy with respect to the existing facial recognition system. The novelty of the proposed method is that it has less computational complexity compared to original SIFT and good accuracy compared to other state-of-the-art methods.
  Keywords: scale invariant feature transform; SIFT; ORL; MOPS; FERET; Yale2B; M2VTS; Extrema.
  DOI: 10.1504/IJBM.2025.10065805
   
  
• Towards biometric template update protocols for cryptobiometric constructions  
  by Subhas Barman 
  Abstract: Biometric data have been stored remotely for authentication purposes. The crypto-biometric construction needs to update the biometric template periodically for security confirmation. However, the revocation of enrolled biometric data requires secure transmission of biometric data from the user to the server. We have analysed three existing schemes where users' biometric templates are stored in the remote server. In the first scheme, a cryptographic key is protected and shared with the biometric data. In the second approach, the biometric template is stored in the server's database and is used to exchange cryptographic keys. In the third scheme, a biometric template is stored and used to derive a permanent key that encrypts the session key and distributes it to the communicating party under a biometric-based key distribution centre. We have analysed the security of all the proposed protocols using the random oracle security model and proved that the protocols are secure against an attacker. We have compared our approaches with the existing approach and found that our third protocol has a minimum communication cost, that is 2,368 bits.
  Keywords: biometric authentication; crypto-biometric frameworks; template update protocol; random oracle; cryptographic key exchange.
  DOI: 10.1504/IJBM.2025.10065177
   
  
• An online noun phrase translation method based on speech recognition technology  
  by Kun Li 
  Abstract: Due to the low translation accuracy of traditional methods, a noun phrase online translation method based on speech recognition technology is proposed. Firstly, an online speech signal recogniser is used to collect the speech signals of noun phrases, and Fourier transform is used for the denoising process. Secondly, based on the denoised speech signal, a benchmark translation context is set to extract the features of noun phrase speech signals in the optimal translation context. Finally, a transformation layer is introduced into the seq2seq model, with the source noun phrase as input and the target noun phrase as output, to construct a neural machine translation model for noun phrases and complete online translation of noun phrases. The experimental results show that the method proposed in this paper can accurately recognise the speech signals of noun phrases and improve the accuracy of online translation. The accuracy of online translation remains above 93%.
  Keywords: speech recognition; noun phrase; online translation; seq2seq model; conversion layer.
  DOI: 10.1504/IJBM.2025.10066023
   
  
• Threshold selection for keystroke dynamics identification system  
  by Onsiri Silasai, Sucha Smanchat, Sirapat Boonkrong 
  Abstract: Keystroke dynamics is the timing information captured when typing on a computer keyboard. It includes hold time and inter-key time. In authentication and identification systems, a threshold is an essential element used in the decision-making process to determine whether a user be granted access or not. Therefore, the threshold selection process is vital. In this work, 15 users were asked to type long texts using a word processor twice a day for 10 days. Two scenarios were used to determine the ability to identify users. EER and accuracy were used to confirm the result and find the most appropriate threshold. The result showed that the highest count thresholds were 0.20 and 0.30. When confirmed by using EER and accuracy, the optimal threshold is 0.20 with an EER of 0.08% and an accuracy of 87.60%. Additionally, our proposed method outperforms those that use free texts to create typing patterns.
  Keywords: keystroke dynamics; threshold selection; user identification.
  DOI: 10.1504/IJBM.2025.10066656
   
  
• Performance analysis on fingerprint identification by deep learning approach  
  by Florence Francis-Lothai, Kung Chuang Ting, Emily Sing Kiang Siew, Hai Inn Ho, Annie Joseph, Tengku Mohd Afendi Zulcaffle, David B.L. Bong 
  Abstract: Achieving high accuracy in fingerprint identification remains challenging, despite various approaches that have been introduced over the years, including deep learning-based methods. These approaches can be computationally complex and may require a vast amount of training data. This study aims to evaluate the performance of deep learning-based approaches for fingerprint identification using two pretrained deep network models, i.e., GoogLeNet and ResNet18. The images in the datasets are first registered and cropped before being trained and validated. The validation rates demonstrated that the preprocessed images produced higher average validation rates compared to the original images. These images are then applied during the testing phase, resulting in nearly perfect identification rates for both models. In comparison, with only 20% of the training dataset, GoogLeNet and ResNet18 achieved 93.00% and 97.00% for the FingerDOS database, respectively. Both models obtained an 88.75% identification rate on the FVC2002 DB1A database, outperforming other methods.
  Keywords: fingerprint identification; biometric; deep learning; GoogLeNet; ResNet18; image registration; speeded up robust features; SURF.
  DOI: 10.1504/IJBM.2025.10067397
   
  

Special Issue on: Applications of Image Processing and Pattern Recognition in Biometrics

• A method for classifying and recognising the emotional states of dancers based on the spatiotemporal features of facial expressions  
  by Yaotian Li, Zhaoping Wang 
  Abstract: To address the issues of low recall and poor accuracy in the classification and recognition of dance performers' emotional states based on spatiotemporal features of facial expressions, a dance performer emotional state classification and recognition method based on spatiotemporal features of facial expressions is proposed. Firstly, face detection is performed using an integral graph, and preprocessing is carried out using affine transformation and histogram equalization. Secondly, combining LBP and LPQ algorithms to extract spatiotemporal features of facial expressions. Next, principal component analysis is applied for feature selection and dimensionality reduction to reduce noise and redundant information. Finally, support vector machine (SVM) is used for emotional state classification, achieving automatic recognition and multi class classification. Through experiments, it has been proven that the accuracy and recall rate of the emotional state recognition method proposed in this paper are high, with a recall rate consistently above 95%.
  Keywords: spatiotemporal features; principal component analysis; PCA; support vector machine; SVM; emotional state; classification recognition.
  DOI: 10.1504/IJBM.2025.10069414
   
  
• A grading evaluation method for English oral pronunciation errors based on deep neural networks.  
  by Jian Sun, Li Zhang, Guanghui Shu 
  Abstract: In this paper, a deep neural network-based grading method for English oral pronunciation errors is proposed. Preprocess English oral pronunciation signals and extract MFCC feature vectors. Using Hidden Markov model to construct an acoustic model, using deep neural network to predict the state probability distribution of acoustic feature vectors, replacing the observation probability of the acoustic model. Construct a language model to obtain the probability of word order, combine it with an acoustic model to build a search network, use the Viterbi algorithm to decode, and find the phoneme state sequence. And based on the reference phoneme sequence, calculate the degree of pronunciation errors, compare it with a threshold, and achieve graded evaluation. The results indicate that the AUC value of the proposed method is close to 1, and the F1 value is above 0.95, indicating a high accuracy of the evaluation.
  Keywords: spoken English; pronunciation error; graded evaluation; hidden Markov model; deep neural network; DNN.
  DOI: 10.1504/IJBM.2025.10069415
   
  
• Research on basketball emergency stop jump shot action recognition based on semantic guided neural network  
  by Yong Wang 
  Abstract: In order to accurately and quickly recognize basketball emergency stop and jump shot movements, a new semantic guided neural network-based basketball emergency stop jump shot action recognition method is proposed. Firstly, improve the quality of basketball action images through color vectorization and filtering preprocessing techniques. Secondly, using image retrieval technology for edge contour feature extraction and fusion retrieval, a high suspicion basketball emergency stop jump shot action pixel feature sample set is selected. Finally, semantic information is integrated into the neural network to improve recognition accuracy. The network architecture innovatively incorporates non local feature extraction modules, ECA attention mechanism modules, and deformable convolution modules to extract feature information. Through fully connected layers, accurate recognition of basketball emergency stop jump shots is achieved. The test results show that the recognition accuracy of this paper method is stable at around 95%, and the highest recognition time is only 0.93s.
  Keywords: semantic guided neural network; basketball emergency stop jump shot; action recognition; edge contour features.
  DOI: 10.1504/IJBM.2025.10069416
   
  
• Seeing the unseen: a novel approach to biometric recognition system  
  by Kumari Deepika , Deepika Punj, Jyoti Verma 
  Abstract: This paper introduces an innovative three-phase cascade framework designed for biometric recognition systems, particularly suited for small-scale applications. By integrating multiple biometric modalities - dorsal vein, wrist vein, and palm print - the framework aims to improve recognition accuracy and robustness. The first phase focuses on extracting unique features from each modality using a moment-based approach that is transformation-invariant and computationally efficient. In the second phase, an asymmetric aggregator operator is employed to merge these features into a unified representation. The final phase utilises spectral clustering to classify and match the fused feature vectors, effectively addressing unseen samples. Evaluated on 350 samples from the COEP and FYO benchmark databases, the framework achieved an impressive accuracy of around 98% for unseen samples, outperforming existing methods like Zernike moment and hierarchical clustering. This work not only enhances biometric authentication but also broadens its applicability across various domains, marking a significant advancement in the field.
  Keywords: moment; unseen samples; spectral clustering; hierarchical; Zernike; Hu; CFOEP palmprint; FYO DB.
  DOI: 10.1504/IJBM.2025.10070135