This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.

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Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

https://www.cse.msu.edu/~rossarun/BiometricsTextBook/Papers/Iris/Bowyer_ImageUndIris_CVIU07.pdf

Image understanding for iris biometrics: A survey

Summary form only given. Biometrics refers to the automatic identification (or verification) of an individual (or a claimed identity) by using certain physical or behavioral traits associated with the person. By using biometrics it is possible to establish an identity based on `who you are?, rather than by `what you possess? (e.g., an ID card) or `what you remember? (e.g., a password). Therefore, biometric systems use fingerprints, hand geometry, iris, retina, face, vasculature patterns, signature, gait, palmprint, or voiceprint to determine a person?s identity. The purpose of this tutorial is two-fold: (a) to introduce the fundamentals of biometric technology from a pattern recognition and signal processing perspective by discussing some of the prominent techniques used in the field; and (b) to convey the recent advances made in this field especially in the context of security, privacy and forensics. To this end, the design of a biometric system will be discussed from the viewpoint of four commonly used biometric modalities - fingerprint, face, hand, and iris. Various algorithms that have been developed for processing these modalities will be presented. Methods to protect the biometric templates of enrolled users will also be outlined. In particular, the possibility of performing biometric matching in the cryptographic domain will be discussed. The tutorial will also introduce concepts in biometric fusion (i.e., multibiometrics) in which multiple sources of biometric information are consolidated. Finally, there will be a discussion on some of the challenges encountered by biometric systems when operating in a real-world environment and some of the methods used to address these challenges.

An introduction to biometrics

Biometric recognition, or simply biometrics, is the science of establishing the identity of a person based on physical or behavioral attributes. It is a rapidly evolving field with applications ranging from securely accessing ones computer to gaining entry into a country. While the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate/graduate students. The three commonly used modalities in the biometrics field, namely, fingerprint, face, and iris are covered in detail in this book. Few other modalities like hand geometry, ear, and gait are also discussed briefly along with advanced topics such as multibiometric systems and security of biometric systems. Exercises for each chapter will be available on the book website to help students gain a better understanding of the topics and obtain practical experience in designing computer programs for biometric applications. These can be found at: http://www.csee.wvu.edu/~ross/BiometricsTextBook/.Designed for undergraduate and graduate students in computer science and electrical engineering, "Introduction to Biometrics" is also suitable for researchers and biometric and computer security professionals.

Introduction to Biometrics

A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate

Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

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Online palmprint identification

—Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

The Resonant Retina: Exploiting Vibration Noise to Optimally Detect Edges in an Image

Automatic personal identification is a significant component of security systems with many challenges and practical applications. The advances in biometric technology have led to the very rapid growth in identity authentication. This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement, and fast search for the best match, we propose a hierarchical multifeature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In our approach, four-level features are defined: global geometry-based key point distance (Level-1 feature), global texture energy (Level-2 feature), fuzzy "interest" line (Level-3 feature), and local directional texture energy (Level-4 feature). In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by a coarse-to-fine guided search. The proposed method has been tested in a database with 7752 palmprint images from 386 different palms. The use of Level-1, Level-2, and Level-3 features can remove candidates from the database by 9.6%, 7.8%, and 60.6%, respectively. For a system embedded with an Intel Pentium III processor (500 MHz), the execution time of the simulation of our hierarchical coding scheme for a large database with 10/sup 6/ palmprint samples is 2.8 s while the traditional sequential approach requires 6.7 s with 4.5% verification equal error rate. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.

/pdf/on-hierarchical-palmprint-coding-with-multiple-features-for-4dm39cjb0b.pdf

On hierarchical palmprint coding with multiple features for personal identification in large databases

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

Detecting Eyelash and Reflection for Accurate Iris Segmentation

Existing approaches to holistic appearance based face recognition require a high dimensional feature space to attain fruitful performance. We have proposed a relatively low feature dimensional scheme to deal with the face recognition problem. We use the aggregated responses of 2D Gabor filters to represent face images. We have investigated the effect of "duplicate" images and the effect of facial expressions. Our results show that the proposed method is more robust than the PCA-based method under varying facial expressions, especially in recognizing "duplicate" images.

/pdf/a-study-of-aggregated-2d-gabor-features-on-appearance-based-433lb6cyfg.pdf

Wai-Kin Kong

Papers

Online palmprint identification

The Resonant Retina: Exploiting Vibration Noise to Optimally Detect Edges in an Image

On hierarchical palmprint coding with multiple features for personal identification in large databases

Detecting Eyelash and Reflection for Accurate Iris Segmentation

A study of aggregated 2D Gabor features on appearance-based face recognition