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Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods. [ABSTRACT FROM AUTHOR]

Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant wavelet transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline dyadic wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively. [ABSTRACT FROM AUTHOR]

Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors.' Although face categorization using common visual cues yields meaningful face categories, developing accurate and robust gender, ethnicity, and age categorizers is a challenging issue. Moreover, it limits the overall number of possible face categories and, in practice, yields unbalanced face categories which can compromise recognition performance. This paper investigates ways to automatically discover a categorization of human faces from a collection of unlabeled face images without relying on predefined visual cues. Specifically, given a set of face images from a group of known individuals (i.e., gallery set), our goal is finding ways to robustly partition the gallery set (i.e., face categories). The objective is being able to assign novel images of the same individuals (i.e., query set) to the correct face category with high accuracy and robustness. To address the issue of face category discovery, we represent faces using local features and apply unsupervised learning (i.e., clustering). To categorize faces in novel images, we employ nearest-neighbor algorithms. or learn the separating boundaries between face categories using supervised learning (i.e., classification). To improve face categorization robustness, we allow face categories to share local features as well as to overlap. We demonstrate the performance of the proposed approach through extensive experiments and comparisons using the FERET database. [ABSTRACT FROM AUTHOR]

This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more "discriminative" bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally). [ABSTRACT FROM AUTHOR]