Robust Face Recognition Using Enhanced Local Binary Pattern

Face recognition is an emerging research area in recognition of the people. A novel feature extraction technique was introduced for robust face recognition. Enhanced Local binary pattern (EnLBP) divided the image into sub regions. For each sub region, the salient features are extracted by obtaining the mean value of each sub region. In LBP, each pixel was replaced by applying LBP into each sub region. In this paper, the mean value of sub region was replaced for the sub region. It reduced the dimension of the image and extracts the salient information on each sub region. The extracted features are compared with similarity measures to recognize the person. EnLBP reduces the operation time and computational complexity of the system. The experimental results were carried out in the standard benchmark database LFW-a. The proposed system achieved a higher recognition rate than other local descriptors

MODIFIED LOCAL TERNARY PATTERN WITH CONVOLUTIONAL NEURAL NETWORK FOR FACE EXPRESSION RECOGNITION

Facial expression recognition (FER) on images with illumination variation and noises is a challenging problem in the computer vision field. We solve this using deep learning approaches that have been successfully applied in various fields, especially in uncontrolled input conditions. We apply a sequence of processes including face detection, normalization, augmentation, and texture representation, to develop FER based on Convolutional Neural Network (CNN). The combination of TanTriggs normalization technique and Adaptive Gaussian Transformation Method is used to reduce light variation. The number of images is augmented using a geometric augmentation technique to prevent overfitting due to lack of training data. We propose a representation of Modified Local Ternary Pattern (Modified LTP) texture image that is more discriminating and less sensitive to noise by combining the upper and lower parts of the original LTP using the logical AND operation followed by average calculation. The Modified LTP texture images are then used to train a CNN-based classification model. Experiments on the KDEF dataset show that the proposed approach provides a promising result with an accuracy of 81.15%

IMPROVEMENT OF ACCURACY OF PARAMETRIC CLASSIFICATION IN THE SPACE OF N×2 FACTORS-ATTRIBUTES ON THE BASIS OF PRELIMINARY OBTAINED LINEAR DISCRIMINANT FUNCTION

A procedure for classifying objects in the space of N×2 factors-attributes that are incorrectly classified as a result of constructing a linear discriminant function is proposed. The classification accuracy is defined as the proportion of correctly classified objects that are incorrectly classified at the first stage of constructing a linear discriminant function. It is shown that, for improperly classified objects, the transition from use as the factors-attributes of their initial values to the use of the centers of gravity (COGs) of local clusters provides the possibility of improving the classification accuracy by 14%. The procedure for constructing local clusters and the principle of forming a classifying rule are proposed, the latter being based on converting the equation of the dividing line to the normal form and determining the sign of the deviation magnitude of the COGs of local clusters from the dividing lin

IMPROVEMENT OF ACCURACY OF PARAMETRIC CLASSIFICATION IN THE SPACE OF N×2 FACTORS-ATTRIBUTES ON THE BASIS OF PRELIMINARY OBTAINED LINEAR DISCRIMINANT FUNCTION

A procedure for classifying objects in the space of N×2 factors-attributes that are incorrectly classified as a result of constructing a linear discriminant function is proposed. The classification accuracy is defined as the proportion of correctly classified objects that are incorrectly classified at the first stage of constructing a linear discriminant function. It is shown that, for improperly classified objects, the transition from use as the factors-attributes of their initial values to the use of the centers of gravity (COGs) of local clusters provides the possibility of improving the classification accuracy by 14%. The procedure for constructing local clusters and the principle of forming a classifying rule are proposed, the latter being based on converting the equation of the dividing line to the normal form and determining the sign of the deviation magnitude of the COGs of local clusters from the dividing lin

Automatic classification of human facial features based on their appearance

[EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. 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Extended Binary Gradient Pattern (eBGP): A Micro- and Macrostructure-Based Binary Gradient Pattern for Face Recognition in Video Surveillance Area

An excellent face recognition for a surveillance camera system requires remarkable and robust face descriptor. Binary gradient pattern (BGP) descriptor is one of the ideal descriptors for facial feature extraction. However, exploiting local features merely from smaller region or microstructure does not capture a complete facial feature. In this paper, an extended binary gradient pattern (eBGP) is proposed to capture both micro- and macrostructure information of a local region to boost up the descriptor performance and discriminative power. Two topologies, the patch-based and circular-based topologies, are incorporated with the eBGP to test its robustness against illumination, image quality, and uncontrolled capture conditions using the SCface database. Experimental results show that the fusion between micro- and macrostructure information significantly boosts up the descriptor performance. It also illustrates that the proposed eBGP descriptor outperforms the conventional BGP on both the patch-based topology and the circular-based topology. Furthermore, a fusion of information from two different image types, orientational image gradient magnitude (OIGM) and grayscale image, attained better performance than using OIGM image only. The overall results indicate that the proposed eBGP descriptor improves the recognition performance with respect to the baseline BGP descriptor