Gender classification (GC) has achieved high accuracy in different experimental evaluations based mostly on inner facial details. However, these results do not generalize well in unrestricted datasets and particularly in cross-database experiments, where the performance drops drastically. In this paper, we analyze the state-of-the-art GC accuracy on three large datasets: MORPH, LFW and GROUPS. We discuss their respective difficulties and bias, concluding that the most challenging and wildest complexity is present in GROUPS. This dataset covers hard conditions such as low resolution imagery and cluttered background. Firstly, we analyze in depth the performance of different descriptors extracted from the face and its local context on this dataset. Selecting the bests and studying their most suitable combination allows us to design a solution that beats any previously published results for GROUPS with the Dago's protocol, reaching an accuracy over 94.2 , reducing the gap with other simpler datasets. The chosen solution based on local descriptors is later evaluated in a cross-database scenario with the three mentioned datasets, and full dataset 5-fold cross validation. The achieved results are compared with a Convolutional Neural Network approach, achieving rather similar marks. Finally, a solution is proposed combining both focuses, exhibiting great complementarity, boosting GC performance to beat previously published results in GC both cross-database, and full in-database evaluations. Evaluation of local descriptors for GC in The Images of Groups datasetBroad evaluation of GC in cross-database scenariosComparison with CNNFusion of local descriptors and CNNAchieving state of the art accuracies in large in the wild datasets
Certainly, the need of facial features restricts the context of application, requiring a visible and almost frontal face. From another point of view, different researchers have recently investigated the inclusion of external facial features @cite_10 @cite_55 such as hair, clothing @cite_35 @cite_47 @cite_8 , their combination with other cues @cite_1 , or even features extracted from the body @cite_16 @cite_28 . The latter claims to be better adapted to real surveillance scenarios where the facial pattern is noisy, not frontal, occluded, or presents low resolution. However, their application is particularly restricted, as no body occlusion may be present.
@cite_35 @cite_8 @cite_28 @cite_55 @cite_1 @cite_47 @cite_16 @cite_10
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Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system. During the last decade, researchers have verified that clothing can provide information for gender recognition. However, before extracting features, it is necessary to segment the clothing region. We introduce a new clothes segmentation method based on the application of the GrabCut technique over a trixel mesh, obtaining very promising results for a close to real time system. Finally, the clothing features are combined with facial and head context information to outperform previous results in gender recognition with a public database. In this paper we study the problem of gender recognition from human body To represent human body images for the purpose of gender recognition, we propose to use the biologically-inspired features in combination with manifold learning techniques A framework is also proposed to deal with the body pose change or view difference in gender classification Various manifold learning techniques are applied to the bio-inspired features and evaluated to show their performance in different cases As a result, different manifold learning methods are used for different tasks, such as the body view classification and gender classification at different views Based on the new representation and classification framework, a gender recognition accuracy of about 80 can be obtained on a public available pedestrian database. We propose a method for recognizing attributes, such as the gender, hair style and types of clothes of people under large variation in viewpoint, pose, articulation and occlusion typical of personal photo album images. Robust attribute classifiers under such conditions must be invariant to pose, but inferring the pose in itself is a challenging problem. We use a part-based approach based on poselets. Our parts implicitly decompose the aspect (the pose and viewpoint). We train attribute classifiers for each such aspect and we combine them together in a discriminative model. We propose a new dataset of 8000 people with annotated attributes. Our method performs very well on this dataset, significantly outperforming a baseline built on the spatial pyramid match kernel method. On gender recognition we outperform a commercial face recognition system. While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos. In this paper, we propose a novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing. Instead of using the whole face, we consider five facial components: forehead, eyes, nose, mouth and chin. We also design feature extraction methods for hair and clothing; these features have seldom been used in previous work because of their large variability. For each type of feature, we train a single support vector machine classifier with probabilistic output. The outputs of these classifiers are combined using various strategies, namely fuzzy integral, maximal, sum, voting, and product rule. The major contributions of this paper are (1) investigating the gender discriminative ability of clothing information; (2) using facial components instead of the whole face to obtain higher robustness for occlusions and noise; (3) exploiting hair and clothing information to facilitate gender classification. Experimental results show that our proposed framework improves classification accuracy, even when images contain occlusions, noise, and illumination changes. In this paper we focus on building robust image representations for gender classification from full human bodies. We first investigate a number of state-of-the-art image representations with regard to their suitability for gender profiling from static body images. Features include Histogram of Gradients (HOG), spatial pyramid HOG and spatial pyramid bag of words etc. These representations are learnt and combined based on a kernel support vector machine (SVM) classifier. We compare a number of different SVM kernels for this task but conclude that the simple linear kernel appears to give the best overall performance. Our study shows that individual adoption of these representations for gender classification is not as promising as might be expected, given their good performance in the tasks of pedestrian detection on INRIA datasets, and object categorisation on Caltech 101 and Caltech 256 datasets. Our best results, 80 classification accuracy, were achieved from a combination of spatial shape information, captured by HOG, and colour information captured by HSV histogram based features. Additionally, to the best of our knowledge, currently there is no publicly available dataset for full body gender recognition. Hence, we further introduce a novel body gender dataset covering a large diversity of human body appearance. We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness-and explore the future potential-of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.