International Journal of Biometrics (5 papers in press)
- 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.
- Implementation of Modified Polar Complex Moments (MPCMS) Based Fingerprint Orientation Estimation for Effective Segmentation
by Nedumaran Damodaran, Selvakumar Mahalingam
Abstract: Segmentation is an important step in deciding the performance of fingerprint identification systems. In this paper, we present the Modified Polar Complex Moments (MPCMs) fingerprint orientation estimation algorithm, capable of describing the fingerprint flow structures including singular point regions in the fingerprint images effectively. To discard the background region of the low-quality fingerprint images, regularization was employed. These algorithms are tested on various types of fingerprint images containing low-quality unrecoverable region and the results obtained from the proposed method were compared with those obtained from well-known gradient based and PCMs methods. The proposed method was also used to study the contrast enhancement process with our previously developed Modified Histogram Equalization (MHE) based on Adaptive Inverse Hyperbolic tangent (AIHT) method. The MPCMs method exhibits better segmentation than the traditional methods in both normal and low-contrast image segmentations, as evident from the estimated matching scores as well as ROC graph.
Keywords: Biometric, Fingerprint segmentation, Contrast enhancement, Orientation field estimation, Zernike moments, FAR, FRR, ERR.
- Face Recognition Based on 2D and 3D Data Fusion
by Paweł Krotewicz, Wojciech Sankowski, Piotr Stefan Nowak
Abstract: The aim of the work presented in this paper is to present current state of the art of face recognition methods and describe proposal algorithms for face biometric identification that analyze 2D face images and 3D face geometry scans. Data for analysis gathered via 3D scanner are processed through different phases. These are: segmentation phase, feature extraction phase and comparison phase. Segmentation relies on localizing characteristic landmark points of the face and projecting the face point cloud onto a plane constructed on the basis of these characteristic points. Feature extraction phase calculates separate feature vectors for 2D and 3D input data. Comparison phase applies fusion of 2D and 3D methods and calculates similarity value between two samples. All samples are compared against one another and results presented as DET curves are generated. By analysis of DET curves, conclusions are formulated.
Keywords: Biometrics; Face Recognition; Biometric Identification; Methods Fusion; Contactless Identification; 3D Face Geometry; Local Binary Patterns; Multimodal Biometrics; Detection Error Tradeoff; Biometric System
- Phoneme dependent inter-session variability reduction for speaker verification
by Haoze Lu, Wenbin Zhang, Yasuo Horiuchi, Shingo Kuroiwa
Abstract: GMM-UBM super-vectors will potentially lead to worse modeling for speaker verification due to the inter-session variability, especially when a small amount of training utterances were available. In this study, we propose a phoneme dependent method to suppress the inter-session variability. A speakers model can be represented by several various phoneme Gaussian mixture models. Each of them covers an individual phoneme whose inter-session variability can be constrained in an inter-session independent subspace constructed by Principal Component Analysis (PCA), and it uses corpus uttered by a single speaker that has been recorded over a long period. SVM-based experiments performed using a large corpus, constructed by the National Research Institute of Police Science (NRIPS) to evaluate Japanese speaker recognition, demonstrate the improvements gained from the proposed method.
Keywords: Inter-session variability;phoneme;speaker verification;principal component analysis