BITS Faculty Publications
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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 Fusion-Based Hand Geometry Recognition Using Dempster–Shafer Theory(World Scientific, 2015) Bera, AsishThis paper presents a new technique for user identification and recognition based on the fusion of hand geometric features of both hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature and also at decision level. Two probability-based algorithms are proposed for classification. The first algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The final decision is considered according to the highest probability as calculated by the Dempster–Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identification and verification. The correct identification rate is found to be 99.5%, and the false acceptance rate (FAR) of 0.625% has been found during verification.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.