Facial Expression Recognition Using Diagonal Crisscross Local Binary Pattern

Facial expression analysis is a noteworthy and challenging problem in the field of Computer Vision, Human-Computer Interaction and Image Analysis. For accomplishing FER, it is very difficult to acquire an effective facial description of the original facial images. The Local Binary Pattern (LBP) which captures facial attributes locally from the images is broadly used for facial expression recognition. But conventional LBP has some limitations. To overcome the limitations, novel approach for Facial Expression Recognition based Diagonal Crisscross Local Binary Pattern (DCLBP). It is based on the idea that pixel variations in diagonal as well as vertical and horizontal (crisscross) should be taken as an image feature in the neighborhood different from the other conventional approaches.The Chi-square distance method is used to classify various expressions. To enhance the recognition rate and reduce the classification time, weighted mask is employed to label the particular components in the face like eyebrow, mouth and eye with larger weights than the other parts of the face. The results of comparison showed the performance of the suggested approach comparing to the other approaches and the experimental results on the databases JAFFE and CK exhibited the better recognition rate

Finger Vein Recognition by Combining Global and Local Features based on SVM

Recently, biometrics such as fingerprints, faces and irises recognition have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins to identify individuals at a high level of accuracy. This paper proposes new algorithms for finger vein recognition. This research presents the following three advantages and contributions compared to previous works. First, we extracted local information of the finger veins based on a LBP (Local Binary Pattern) without segmenting accurate finger vein regions. Second, the global information of the finger veins based on Wavelet transform was extracted. Third, two score values by the LBP and Wavelet transform were combined by the SVM (Support Vector Machine). As experimental results, the EER (Equal Error Rate) was 0.011 % and the total processing time was 98.2ms

Face recognition using color local binary pattern from mutually independent color channels

In this paper, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDF) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform (DWT) and singular value decomposition (SVD). After equalization, face images are segmented by use of local Successive Mean Quantization Transform (SMQT) followed by skin color based face detection system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and thestate-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60% and 68.80% for conventional gray scale LBP and Principle Component Analysis (PCA) based face recognition techniques respectively.Comment: 11 pages in EURASIP Journal on Image and Video Processing, 201

Face Authentication with Salient Local Features and Static Bayesian Network

In this paper, the problem of face authentication using salient facial features together with statistical generative models is adressed. Actually, classical generative models, and Gaussian Mixture Models in particular make strong assumptions on the way observations derived from face images are generated. Indeed, systems proposed so far consider that local observations are independent, which is obviously not the case in a face. Hence, we propose a new generative model based on Bayesian Networks using only salient facial features. We compare it to Gaussian Mixture Models using the same set of observations. Conducted experiments on the BANCA database show that our model is suitable for the face authentication task, since it outperforms not only Gaussian Mixture Models, but also classical appearance-based methods, such as Eigenfaces and Fisherfaces

On the Recent Use of Local Binary Patterns for Face Authentication

This paper presents a survey on the recent use of Local Binary Patterns (LBPs) for face recognition. LBP is becoming a popular technique for face representation. It is a non-parametric kernel which summarizes the local spacial structure of an image and it is invariant to monotonic gray-scale transformations. This is a very interesting property in face recognition. This probably explains the recent success of Local Binary Patterns in face recognition. In this paper, we describe the LBP technique and different approaches proposed in the literature to represent and to recognize faces. The most representatives are considered for experimental comparison on a common face authentication task. For that purpose, the XM2VTS and BANCA databases are used according to their respective experimental protocols