Department of Computer Science and Information Systems
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Item Deep Ear Biometrics for Gender Classification(Springer, 2023-07) Bera, AsishHuman gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset.Item Hand Biometric Verification with Hand Image-Based CAPTCHA(Springer, 2018-05) Bera, AsishAn approach for hand biometric recognition with the hand image-based CAPTCHA verification is presented in this paper. A new method for CAPTCHA generation is implemented based on the genuine and fake hand images which are embedded in a complex textured color background image. The HandCaptcha is a useful application to differentiate between the human and automated scripts. The first level of security is achieved by the HandCaptcha against the malicious threats and attacks. After solving the HandCaptcha correctly, the identity of a person is authenticated based on the contact-less hand geometric verification approach in the second level. A set of 300 unique HandCaptcha is created randomly and solved by at least 100 persons with the accuracy of 98.34%. Next, the left-hand images of the legitimate users are normalized, and sixteen geometric features are computed from every normalized hand. Experiments are conducted on the 200 subjects of the Bosporus left-hand database. Classification accuracy of 99.5% has been achieved using the kNN classifier, and the equal error rate is 3.93%.Item Pose-Invariant Hand Geometry for Human Identification Using Feature Weighted k-NN Classifier(Springer, 2018-05) Bera, AsishHand biometrics is globally deployed for automated human identification based on the discriminative geometric characteristics of hand. Advancements in hand biometric technologies are accomplished over several decades. The key objectives of this paper are two-fold. Firstly, it presents a comprehensive study on the state-of-the-art methods based on the hand images collected in an unconstraint environment. Secondly, a pose-invariant hand geometry system is excogitated. The experiments are conducted with the weighted geometric features computed from the fingers. The feature weighted k-nearest neighbor (fwk-NN) classifier is applied on the right- and left-hand images of the 500 subjects of the Bosphorus database for performance evaluation. The classification accuracy of 97% has been achieved for both of the hands using the fwk-NN classifier. Equal error rates (EER) of 5.94% and 6.08% are achieved for the right- and left-hand 500 subjects, respectively.Item Person recognition using alternative hand geometry(Inder Science, 2014-08) Bera, AsishIn this paper, a new approach for user recognition is presented, which is based on the geometric features from either left or right hand images. The hand images are collected at unconstrained pose environment. Image normalisation is applied at the preprocessing stage. Features are extracted from the normalised images, which are mainly comprised of lengths and widths at different positions of the fingers. A simple classification algorithm has been implemented that is primarily dependent on the ratio of modified minimum distance and number of features, which are matched within a distance threshold. Experimental results of identification and verification are quite acceptable, producing 98.8% identification and 99.6% verification (at 0.55% FAR) of 253 standard subjects which are a blend of both left and right hand images.Item Finger contour profile based hand biometric recognition(Springer, 2016-10) Bera, AsishThis paper presents a contactless hand biometric system at unrestricted hand pose environment. A new preprocessing technique is proposed for defining the finger contour profiles (FCP). It mainly consists of simple grayscale image transformation, subtraction, and logical XOR operation. This hand prototyping method logically decomposes global hand contour into the left and right contour profiles of each finger. A set of twenty pose-invariant geometric features is extracted from the FCP and normalized global hand shape. Experiments are conducted on two publicly available hand databases namely, the Bosphorus and IIT Delhi (IITD) databases to validate the system using the kNN, minimum distance, and random forest (RF) classifiers. Satisfactory identification accuracy of 97.82 % using the RF classifier has been achieved for the Bosphorus database with 320 subjects; and in verification, 3.28 % equal error rate (EER) is reported. The kNN classifier has been found to produce good identification success of 95.22 % for the IITD database of 230 subjects; and 4.76 % EER is obtained in verification. The average execution time of this approach is lesser than 2 s, that implies its suitability in real-world applications.