Racial Classification from Face

2011

Humans are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, including race, gender , and age. Experimental results are based on a face database containing subjects. Race and gender also play an important role in face-related applications. Experimental results are indicated that participants categorized the race of the face and this categorization drives the perceptual process. A face image data set is collected from Internet, and divided into a training dataset and a test dataset. Experimental results based on a face database containing 250 subjects. The proposed system can also be applied to other image-based classification tasks.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019

In the field of demographic attribute classification, race estimation is perhaps the least studied topic in the literature. CNN-based approaches report the best results to the day, but they are computational expensive for practical applications. We propose a simpler approach by combining local appearance and geometrical features to describe face images, and to exploit the race information from different face parts by means of a component-based methodology. Experimental results obtained in the FERET subset from EGA database, with traditional but effective classifiers like Random Forest and Support Vector Machines, are very close to those achieved with a recent deep learning proposal.

2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings, 2012

Research community achieved considerable progress in face recognition over the past years. Despite this, present face recognition systems are not yet accurate or robust enough to be fully deployed in under-controlled yet high security environments. A number of works have investigated the impact of face categorization on recognition performance, in order to assess the hypothesis that a preliminary face categorization can be used to contain the search space during identification. Categories are usually related to soft-biometrics, such as gender, age, ethnicity. More features can also be used at the same time to define categories (e.g. gender and age). The underlying assumption is that, during identification operations, a sample image is only matched with those pertaining to the same category. Experimental results demonstrate that face categorization based on important visual characteristics such as gender, ethnicity, and age generally improve recognition accuracy, while reducing operation time. On the other hand, it is difficult to appropriately set up related experiments, since available datasets are not organized according to any categorization. Moreover, it is often the case that some features (e.g. ethnicity or gender) are not uniformly represented. For instance, the ethnicity of the research group gathering a dataset, and therefore the location where the enrollment operations are performed, often influences the prevailing ethnical composition of the dataset. As a further example, since most datasets are gathered by enrolling volunteer students, the prevailing age range in most datasets is 20-35. Our contribution relies in an automatic procedure to build a larger multi-racial database, starting from the most popular among the available ones, which automatically reproduces the ethnicity/gender/age categorization that we manually performed in our lab.

Advances in Science, Technology and Engineering Systems Journal

Face explicitly provides the direct and quick way to evaluate human soft biometric information such as race, age and gender. Race is a group of human beings who differ from human beings of other races with respect to physical or social attributes. Race identification plays a significant role in applications such as criminal judgment and forensic art, human computer interface, and psychology science-based applications as it provides crucial information about the person. However, categorizing a person into respective race category is a challenging task because human faces comprise of complex and uncertain facial features. Several racial categorization methods are available in literature to identify race groups of humans. In this paper, we present a comprehensive and comparative review of these racial categorization methods. Our review covers survey of the important concepts, comparative analysis of single model as well as multi model racial categorization methods, applications, and challenges in racial categorization. Our review provides state-of-the-art technical information concerning racial categorization and hence, will be useful to the research community for development of efficient and robust racial categorization methods.

2017 International Conference on Cyberworlds (CW), 2017

2012

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 Ghulam et al. 2 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).

International Review of Social Psychology, 2018

Categorising other people is one of the core functions of social cognition and one of the most prevalent pieces of information we use in this endeavour is the face. Relying on faces is particularly likely when to-be-categorised groups have distinctive facial features, as is the case with some ethnic groups, like Black and White individuals in many countries or Caucasian and North African individuals in Europe. Accordingly, when possible, studies designed to capture early categorisation processes rely on facial prototypes of some ethnic groups. This is often the case, for instance, with the most popular indirect measure, the implicit association test (IAT; Greenwald, McGhee & Schwartz, 1998), which can be found on the Project Implicit website (Nosek, Banaji & Greenwald, 2002). However, using faces for social categorisation purposes is only possible when a relevant face database is available. For instance, in France and the US, the Black/White IAT relies on faces, but the Arab-Muslim IAT relies on first names, which obviously precludes face-related categorisation processes for this latter group. The goal of the current contribution is therefore to provide a face database, the Caucasian and North African French Faces (CaNAFF), designed to be used when scholars are interested in using Caucasian and North African faces corresponding to faces that can be encountered in France. Faces in Intergroup Relations It is now well established that faces provide a lot of information for making assumptions about people and their characteristics (e.g. Oosterhof & Todorov, 2008; Willis & Todorov, 2006). In the domain of intergroup relations, extracting visual features from faces is essential to categorise others in specific groups (e.g. gender, ethnicity, age) and to combine these features with other (face-related or contextual) features (e.g. Black faces perceived as more threatening when they display a direct eye-gaze direction, but less so when displaying averted eye-gaze direction; Trawalter, Todd, Baird & Richeson, 2008). Given the importance of faces to studying categorisation and social perception in a given cultural context, various face databases have been developed. Many of these databases focus on one or several dimensions, like faces of different ages or displaying various emotions (e.g.

2014

This paper presents a novel human skin color classification into skin color tones: White and Black. This is performed by developing a skin color classifier based on pixelbased classification using RGB model. Our proposed method is classified under the category of an explicitly defined skin region model. The skin classifier divides our database formed by some images from the FERET set of faces into two subdatabases according to the skin color. The skin color classification method is then applied on a face recognition technique by reducing the number of trained images in the matching process. The performance of the proposed human skin color classifier is evaluated perceptually. Experimental results showed that our proposed skin color classifier is able to classify a face into its possible skin color tone and reaches 87% as hit rate.

ArXiv, 2021

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a raceclassification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on ...

Information, 2017

Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.