The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

位相情報に基づく生体認証に関する研究

Tohoku University青木孝文課

Framework for reliable, real-time facial expression recognition for low resolution images

International audienceAutomatic recognition of facial expressions is a challenging problem specially for low spatial resolution facial images. It has many potential applications in human-computer interactions, social robots, deceit detection, interactive video and behavior monitoring. In this study we present a novel framework that can recognize facial expressions very efficiently and with high accuracy even for very low resolution facial images. The proposed framework is memory and time efficient as it extracts texture features in a pyramidal fashion only from the perceptual salient regions of the face. We tested the framework on different databases, which includes Cohn-Kanade (CK+) posed facial expression database, spontaneous expressions of MMI facial expression database and FG-NET facial expressions and emotions database (FEED) and obtained very good results. Moreover, our proposed framework exceeds state-of-the-art methods for expression recognition on low resolution images

LBP based on multi wavelet sub-bands feature extraction used for face recognition

The strategy of extracting discriminant features from a face image is immensely important to accurate face recognition. This paper proposes a feature extraction algorithm based on wavelets and local binary patterns (LBPs). The proposed method decomposes a face image into multiple sub-bands of frequencies using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping sub-regions. Then LBP histograms based on the traditional 8-neighbour sampling points are extracted from the approximation sub-band, whilst 4-neighbour sampling points are used to extract LBPHs from detail sub-bands. Finally, all LBPHs are concatenated into a single feature histogram to effectively represent the face image. Euclidean distance is used to measure the similarity of different feature histograms and the final recognition is performed by the nearest-neighbour classifier. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combination of sub-bands. Results show that the proposed method outperforms the traditional LBP based features

Face Recognition using Multi Region Prominent LBP Representation

Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s  is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods

AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits