International Journal of Biometrics (6 papers in press)
Partial Silhouette-based Gait Recognition
by Soharab Hossain Shaikh, Khalid Saeed, Nabendu Chaki
Abstract: Gait analysis refers to identification of a person from the systemic study of the motions of his/her different body parts at the time of walking. It is one of the behavioural biometric approaches for human identification. Like other behavioural approaches, gait also suffers from low-repeatability leading to poor recognition accuracy. Therefore, multimodal systems are required for better recognition accuracy. With the advent of multimodal biometric systems there is a need for low-cost methods for individual biometric modalities so that the overall complexity of the system does not overshoot the real-time requirements. In view of this, a partial-silhouette-based approach for gait recognition is reported in this article. This approach is translation, rotation and scale invariant and is low-cost in terms of computational complexity. Experimental results and comparative performance analysis on benchmark dataset reveal the potential of the partial-silhouette-based approach.
Keywords: Gait analysis; appearance-based approach; human identification; partial-silhouette-based approach; gait biometrics.
A Clustering based Indexing Approach for Biometric Databases using Decision-Level Fusion
by Ilaiah Kavati, Munaga VNK Prasad, Chakravarthy Bhagvati
Abstract: In this paper, we proposes a clustering-based indexing mechanism for biometric databases. The proposed technique relies mainly on a small set of preselected images called representative images. First, the database is partitioned into set of clusters and one image from each cluster is selected for the representative image set. Then, for each image in the database, an index code is computed by comparing it against the representative images. Further, an efficient storage structure (i.e., index space) is developed and the biometric images are arranged in it like traditional database records so that a quick search is possible. During identification, a list of candidates which are very similar to the query are retrieved from the index space. Further, to make full use of the clustering, we also retrieve the candidate identities from the selected clusters which are similar to query. Finally, the candidate identities from the index space and cluster space are fused using decision-level fusion. Experimental results on different databases shows a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods.
Keywords: Clustering; Indexing; Representative images; Match scores; Decision-level fusion; Palmprints; Hand veins.
Extraction and selection of Binarized Statistical Image Features for fingerprint recognition
by Ahlem Adjimi, Abdenour Hacine-Gharbi, Philippe Ravier, Messaoud Mostefai
Abstract: Due to their simplicity and efficiency, image-based descriptors are currently very used in the task of fingerprint recognition. Among these descriptors, the histogram based descriptors are of widespread use. In this work, we use a novel histogram based descriptor called Binarized Statistical Image Features (BSIF). The gray value of each pixel is passed through a data-learned linear filter which output is converted into a binary code string. BSIF is a texture descriptor similar to the Local Binary Patterns (LBP) and Local Phase Quantization (LPQ), but the difference is the way the filters are learned. The BSIF descriptor depends on two main parameters which are the filter window size and the number of bits in the binary code string. In this work, we have evaluated the BSIF descriptor on the standard FVC2002 DB1 and DB4 databases, using different filter sizes with different bit lengths. We have extracted the BSIF histograms from sub-images around the core point of the fingerprint image and concatenated them to construct the final features vector. The experiments have shown that an increasing number of extracted sub-images lead to an increasing recognition rate. But increasing the number of sub-images also leads to higher dimension histogram estimations which decreased performance of the system regarding computing time and memory capacity. To avoid this problem we have used a feature selection method called Interaction capping (ICAP) which selects the relevant bins of the BSIF histogram based on the mutual information measure. This procedure permits to reduce the dimensionality of the BSIF histogram. The obtained results clearly showed that using the BSIF descriptor as a novel fingerprint features extraction method had a good effect on the recognition rates which outperformed the rates obtained with LBP or LPQ descriptors except for the smallest code string (length of 5). The results also showed that using feature selection method could reduce the dimensionality leading to a less computational complexity of the system.
Keywords: Biometrics; fingerprints; fingerprint identification;features extraction; BSIF; LBP; LPQ; texture descriptors; histogram; recognition rate; feature selection; mutual information; ICAP.
A fingerprint matching algorithm using bit-plane extraction method with phase-only correlation
by Florence Francis-Lothai, David B.L. Bong
Abstract: This paper introduces a new method in fingerprint feature extraction based on bit-plane. A bit-plane image requires smaller storage space than a grayscale image. Region of Interest (ROI) of a fingerprint image is extracted by using a modified blob analysis method, then the core point of the ROI is detected for dimension reduction process. Before bit-plane is extracted, the fingerprint image is enhanced by using Fourier transform. Bit-plane 7 of the enhanced image is used as the input for fingerprint matching with Phase-Only Correlation (POC) function. Experiment results showed that the storage space requirement for a fingerprint database could be reduced up to 87% per image for a bit-plane image compared with a grayscale image. The proposed fingerprint matching algorithm achieves 81.16% of recognition rate on FVC2002-Db1a database and 89.78% on FingerDOS database.
Keywords: bit-plane; biometric; feature extraction; fingerprint matching algorithm; Phase-Only correlation.
An Approach for Facial Expression Classification
by Ali Muhamed Ali, Hanqi Zhuang, Ali Ibrahim
Abstract: Human facial expression classification has attracted great attention in the field of computer vision and pattern recognition over the past decades. This is partly due to peoples curiosity in exploring this fascinating research area and partly due to its potential applications. In this paper, a new method for facial expression classification is proposed. The method uses the Histograms of Oriented Gradients (HOG) algorithm to extract facial expression features and the Sparse Representation Classifier (SRC) to classify facial expressions with a large variation of poses. The Histograms of Oriented Gradients algorithm was selected due to its effectiveness in picking up both local and global facial expression features in different orientations and scales, and the Sparse Representation Classifier was chosen because of its proven effectiveness in face recognition. A novelty of the proposed approach is that given a facial image for classification, its pose is determined first in order to select a pose-dependent dictionary for the SRC procedure. The paper also discusses in detail how to select parameters to improve the effectiveness of the HOG algorithm. The proposed method was applied to two multi-pose facial expression databases: KDFE and RaFD, and satisfactory results were obtained for a majority of facial expressions under various poses.
Keywords: Facial expression; expression classification; Histograms of Oriented Gradients; emotion detection; Sparse Representation Classifier.
Fingerprint Representation and Matching for Secure Smartcard Authentication
by Ibrahim El-leithy, Gouda Salama, Tarek Mahmoud
Abstract: In this paper, a light weight fingerprint matching algorithm (4 KB) is proposed. This algorithm is based on matching features that are invariant to major transformations like translation and rotation. The algorithm can be executed on devices with low computing power and limited memory size. Thus, the matching algorithm is implemented on smartcard over the Java Card TM platform. The algorithm has an asymmetric terminating behavior. Therefore, the execution time varies depending on correct positive matches (similar fingerprint) and correct negative matches (dissimilar fingerprint). In this paper, two fingerprint authentication methods are implemented. The first fingerprint authentication method is applied using one reference and one candidate fingerprints. However, the second method is performed by the aid of a fusion based fingerprint authentication manner using two reference and one candidate fingerprints. The performance of the methods in terms of authentication accuracy is tested on some standard databases from the Fingerprint Verification Competition 2002 (FVC2002).
Keywords: Biometric Authentication ; Fingerprint Matching ; Fusion at the Decision Level ; Match on Card ; Smartcard.