International Journal of Biometrics (7 papers in press)
- A Secure Palmprint Authentication System using Chaotic Mixing and Watermarking
by Munaga V N K Prasad, Ilaiah Kavati
Abstract: In recent years, there has been a significant increase in the use of biometrics for user authentication. The wide spread use of biometrics in real life applications for user authentication raised the security and privacy concerns of the biometric data. In this paper, a biometric image protection scheme exploiting the properties of chaotic mixing and watermarking technique is proposed. We also proposed an efficient technique for biometric template protection using a random vector. A detailed discussion on the security of the proposed techniques are given. The effectiveness of the proposed method is investigated with application to palmprint authentication. The experimental results evaluated on PolyU and IITD database shows that the proposed system achieves better recognition accuracy and lower error rates while providing protection to the biometric data.
Keywords: Image security; Template security; Palmprint; Chaotic mixing, Watermarking; Random vector
- Integration Framework for Assembling Components of a Face Recognition System
by Monpraon Sukroongreung, Pratit Santiprabhob
Abstract: This paper outlines a framework that has been developed to assist in integrating components of a given face recognition system. By applying this framework, performance and restrictions of each component can be explicitly observed. Recognition performance of the resulting face recognition system can be clearly compared with components are replaced by other alternatives. The contribution of our work is a generalized framework for components integration of a face recognition system that facilitates (i) examination of the effects of varying techniques and components to the recognition performance; and (ii) determination of suitable techniques, components and system configurations as per a given problem context. We have applied our framework to assemble different alternatives of a face recognitions system. Such applications reveal the effects when different components are included into a face recognition system and have shown insights into the relationship between dataset quality, techniques selected and recognition performance. This paper also outlines how the proposed framework can be applied to other biometric recognition systems.
Keywords: integration frameworks, component selections, biometrics
- ENHANCING THE PERFORMANCE OF TEXTURE BASED FACE RECOGNITION THROUGH MULTIRESOLUTION TECHNIQUES
by Meena Nandhini, Suruli Andadvar, Reenarose R
Abstract: Automatic face recognition is an emerging active research area spanning several disciplines such as image processing, computer vision and pattern recognition. Face recognition is a challenging problem because of diversity in faces and variations caused by expressions, illuminations, pose, occlusion, aging and so on. In this paper, multiresolution techniques are combined with texture features to mitigate the effect of facial variations. Multiresolution techniques investigated in this paper are Discrete Wavelet Transform (DWT), Ridgelet, Curvelet and Contourlet. Texture features are extracted from these transforms by using Local Binary Pattern (LBP), Local Texture Pattern (LTP), Local Derivative Pattern (LDP), Local Tetra Patterns (LTrPs) and Local Derivative Ternary Pattern (LDTP). The proposed method is tested on JAFFE, ORL, Yale, Essex and Georgia tech databases containing more than 4000 face images. From the results, it is observed that, the combined approach of multiresolution techniques with texture features enhances the face recognition rate. In particular, Contourlet transform with LDTP perform better than the other techniques considered for investigation.
Keywords: Biometrics; face recognition; texture analysis; texture features; multiresolution analysis; G statistics; K Nearest Neighbor; KNN.
- Compressed Phase Component for Low Resolution Face Recognition
by Naser Zaeri
Abstract: Most of the current algorithms for face recognition do not consider images of low resolution that commonly exist in real life applications. In this paper, we address this issue and propose an efficient solution for face recognition for systems that deal with such images and utilize limited storage. The new method transforms the input data into a different form that identifies the face image database structure, for which certain data can be dropped (or compressed) without the fear of performance deterioration. It implements a lossy compression scheme on the discriminant Fourier phases of the face image components. A thorough study and comprehensive experiments relating to time consumption and computational complexity versus system performance are applied to benchmark face image databases. It will be shown that the proposed method offers exceptional performance and, at the same time, achieves substantial savings in computational time when compared to other known methods. The experimental results reveal that a recognition rate higher than 98% is achieved at a compression ratio of 1.78, with training time less than four minutes for a database consisting of 2,360 images.
Keywords: Face recognition, Fourier phase, low resolution, compression ratio
- Face Recognition using Multiple Content-Based Image Features for Biometric Security Applications
by Madeena Sultana, Marina Gavrilova
Abstract: During the era of Internet, Content-Based Image Retrieval (CBIR) systems, where images are searched based on their visual contents, have an increasing demand for numerous real world applications. However, the potential of using multiple CBIR based features for biometric recognition remains largely unexplored. This research presents an in-depth analysis of current research trends of CBIR and its potential applications in the field of biometric security. A novel content-based face recognition system is proposed and experimental results are provided to strengthen the material of this article. In the proposed face recognition system, three content-based low level features: color, texture, and shape are combined to enhance the recognition accuracy. Moreover, the simplicity and ease of computation of the exploited methods reduce computation time. Experimental results show that the proposed multiple low level feature based method outperforms single feature based face recognition systems.
Keywords: Face Recognition, Content-Based Image Retrieval (CBIR), Color Histogram, Gabor Filter, Histogram Intersection, Affine Moment Invariant, Pseudo-Zernike Moment Invariant.
- Palmprint Identification and Verification based on wide principal lines through Dynamic ROI
by Hemantha Kalluri, Munaga Prasad, Arun Agarwal
Abstract: In this paper, a novel palmprint identification and verification algorithm is proposed based on wide principal lines through Dynamic ROI. Region of interest (ROI) extraction is an important task for palmprint identification. Earlier reported works used fixed size ROI for the recognition of palmprints. When the fixed size ROI is used the palm area taken up for recognition is less compared to dynamic ROI extraction. The proposed algorithm focuses on extraction of maximum possible ROI. A set of wide principal line extractors are devised. Later these wide principal line extractors are used to extract the wide principal lines from Dynamic ROI. A two stage palmprint identification algorithm is proposed based on wide principal lines. The experimental results demonstrate that the proposed approach extracts better ROI on the PolyUPalmprint Database when compared to the existing fixed size and dynamic size ROI extraction techniques. The experimental results for the verification and identification on PolyUPalmprint Database show that the discrimination of wide principal lines is also strong.
Keywords: Palmprint; Biometrics; key points; midpoint; energy value; region of interest; Principal lines; wide principal line extractor; Palmprint verification, Palmprint identification.
- Face Verification Using Local Binary Patterns and Generic Model Adaptation
by Elhocine Boutellaa, Messaoud Bengherabi, Samy Ait-Aoudia, Abdenour Hadid, Farid Harizi
Abstract: The popular Local binary patterns (LBP) have been highly successful in representing and recognizing faces. However, the original LBP based face recognition method has some problems that need to be addressed in order to increase its robustness and discriminative power and to make the methodology suitable for the needs of different types of applications. For instance, a drawback of the LBP representations using histograms concerns the number of entries in the histograms as a too small number of bins would fail to provide enough discriminative information about the face appearance while a too large number of bins may lead to sparse and unstable histograms. In this work, we propose two approaches to address the limitations of the original LBP based face verification. In the first approach, we propose a solution using vector quantization maximum a posteriori adaptation (VQMAP) model, in which a generic face model is obtained by vector quantization and the user models are inferred using maximum a posteriori adaptation. In the second approach, we propose an enhanced LBP histogram representation which generates more robust and reliable face feature vectors. We adapt a generic face histogram to each face, making use of the shared face information in the generic face to improve the representation. The two proposed approaches are further fused to enhance the verification system performance. We extensively evaluate our proposed approaches on two publicly available benchmark databases, namely BANCA and XM2VTS databases, and compare the results against not only the original LBP approach but also other LBP variants, demonstrating very promising results.
Keywords: Face verification; Local binary patterns; Vector quantization maximum a posteriori adaptation; Histogram adaptation.