Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence

Abstract

One of the main steps in human identification is gender classification which can increase the identification accuracy. In many smart phone applications, human identification plays an important role in different reasons such as login permission, sign up certificates, etc. So, accurate gender classification algorithms may increase the accuracy of smart phone applications and reduce its complexity. Also, one of the benefits of gender classification algorithms is for parents to monitor the social network contacts of their child in terms of gender. Different methods have been proposed to do it accurately so far. In all methods, classification accuracy is the main challenge for researchers. But, in smart phone applications, some challenges such as rotation, gray scale variations may reduce the accuracy. In this respect, a rotation invariant approach is proposed in this paper to classify genders in human face images based on improved version of local binary patters (ILBP). Local binary pattern (LBP) is a texture descriptor, which extract local contrast and spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. ILBP solves the above disadvantages using a new theory for binary patterns categorization. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on ILBP. Next, Kullback-Leibler divergence classifier is used to classify gender. In this paper Kullback Leibler classifier is evaluated based on log likelihood ratio as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by different well known methods in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Other main advantages of our approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smartphones because of memory and CPU usage reduction.

Energy analysis of image descriptors in texture classification

“…In a same way like previous steps, the energy amount of the edge detected image is computed. In this point, every image has a feature vector like F. It is shown equation (4). F=< Energy (MBP), Energy (LBP), Energy (GLCM)> (4) In the second stage, the energy amount of original texture is computed.…”

“…In this point, every image has a feature vector like F. It is shown equation (4). F=< Energy (MBP), Energy (LBP), Energy (GLCM)> (4) In the second stage, the energy amount of original texture is computed. Next, it is compared by every dimensions of feature vector F. So, a feature vector like F′ would provide which has meaningful information about input image and can use for classification this kind of textures.…”

“…In last decade, and with the progress of the computer vision and image processing, texture classification turned out to be one of the main subjects in the literature of these sciences. As texture classification has a close relationship with the sciences such as machine learning, it functions in areas like face recognition [2], skin detection [3], gender classification [4], visual pattern recognition [5], object tracking [6], human identification [7], surface defect detection [8], image retrieval [9], medical image diagnosis [10], etc. The main challenges in texture classification relates to two points as follows: introducing the most discriminative features and selecting the most consistent classifier by extracted features.…”

An Image Recognition Algorithm for Defect Detection of Underground Pipelines Based on Convolutional Neural Network

“…Image recognition plays an important role in various aspects of our lives. For example, fingerprint recognition system [3] and face recognition system [4] are widely adopted for identity recognition; in agronomy, the growth condition and pathological changes of plants are judged by the leaf shape and spot type in their images [5].…”

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

“…We also know that to have fairly important compression rates, we must use lossy compression techniques, which essentially induce changes of information in the compressed images and create considerable degradation during the exploitation of the reconstructed images [2]. The foremost important concern will therefore be to prevent this degradation from affecting the final use of those images; in diagnostic in medical images and identification/authentication in biometric images; like identification and diagnostic in medical images [3] and classification, identification, or authentication in biometric images [4].…”