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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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. A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers. In many surveillance systems there is a requirement todetermine whether a given person of interest has alreadybeen observed over a network of cameras. This is the personre-identification problem. The human appearance obtainedin one camera is usually different from the ones obtained inanother camera. In order to re-identify people the humansignature should handle difference in illumination, pose andcamera parameters. We propose a new appearance modelbased on spatial covariance regions extracted from humanbody parts. The new spatial pyramid scheme is applied tocapture the correlation between human body parts in orderto obtain a discriminative human signature. The humanbody parts are automatically detected using Histograms ofOriented Gradients (HOG). The method is evaluated usingbenchmark video sequences from i-LIDS Multiple-CameraTracking Scenario data set. The re-identification performanceis presented using the cumulative matching characteristic(CMC) curve. Finally, we show that the proposedapproach outperforms state of the art methods. Facial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21].