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
In this paper, a first objective is to analyze GC results of different datasets to identify those that are closer to real scenarios. As mentioned above, most state-of-the-art GC approaches focus on the facial pattern. This is evidenced by the latest problem surveys @cite_39 @cite_2 , and recent results in major journals @cite_6 @cite_5 @cite_42 @cite_36 @cite_51 @cite_0 @cite_11 .
@cite_36 @cite_42 @cite_6 @cite_39 @cite_0 @cite_2 @cite_5 @cite_51 @cite_11
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Gender recognition from face images has many applications and is thus an important research topic. This paper presents an approach to gender recognition based on shape, texture and plain intensity features gathered at different scales. We also propose a new dataset for gender evaluation based on images from the UND database. This allows for precise comparison of different algorithms over the same data. The experiments showed that information from different scales, even if just from a single feature, is more important than having information from different features at a single scale. The presented approach is quite competitive with above 90 accuracy in both evaluated datasets. We automatically assemble a big dataset to train a face gender classifier.This is formed by 4 million images and over 60,000 features.The resulting system significantly outperforms the previous state of the art without human annotation.This study lends support to the "unreasonable effectiveness of data" conjecture.This study is relevant to computer vision (LBP features, face classification), machine learning (large scale linear classifiers), and big data.This study can serve as a template for other "web scale" learning tasks. The application of learning algorithms to big datasets has been identified for a long time as an effective way to attack important tasks in pattern recognition, but the generation of large annotated datasets has a significant cost. We present a simple and effective method to generate a classifier of face images, by training a linear classification algorithm on a massive dataset entirely assembled and labelled by automated means. In doing so, we perform the largest experiment on face gender recognition so far published, reporting the highest performance yet. Four million images and more than 60,000 features are used to train online classifiers. By using an ensemble of linear classifiers, we achieve an accuracy of 96.86 on the most challenging public database, labelled faces in the wild (LFW), 2.05 higher than the previous best result on the same dataset (Shan, 2012). This result is relevant both for the machine learning community, addressing the role of large datasets, and the computer vision community, providing a way to make high quality face gender classifiers. Furthermore, we propose a general way to generate and exploit massive data without human annotation. Finally, we demonstrate a simple and effective adaptation of the Pegasos that makes it more robust. Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice. Applications such as human---computer interaction, surveillance, biometrics and intelligent marketing would benefit greatly from knowledge of the attributes of the human subjects under scrutiny. The gender of a person is one such significant demographic attribute. This paper provides a review of facial gender recognition in computer vision. It is certainly not a trivial task to identify gender from images of the face. We highlight the challenges involved, which can be divided into human factors and those introduced during the image capture process. A comprehensive survey of facial feature extraction methods for gender recognition studied in the past couple of decades is provided. We appraise the datasets used for evaluation of gender classification performance. Based on the results reported, good performance has been achieved for images captured under controlled environments, but certainly there is still much work that can be done to improve the robustness of gender recognition under real-life environments. In this paper, we report our extension of the use of feature selection based on mutual information and feature fusion to improve gender classification of face images. We compare the results of fusing three groups of features, three spatial scales, and four different mutual information measures to select features. We also showed improved results by fusion of LBP features with different radii and spatial scales, and the selection of features using mutual information. As measures of mutual information we use minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), conditional mutual information feature selection (CMIFS), and conditional mutual information maximization (CMIM). We tested the results on four databases: FERET and UND, under controlled conditions, the LFW database under unconstrained scenarios, and AR for occlusions. It is shown that selection of features together with fusion of LBP features significantly improved gender classification accuracy compared to previously published results. We also show a significant reduction in processing time because of the feature selection, which makes real-time applications of gender classification feasible. We used both inner and outer face cues.External cues improve classification performance for gender recognition.FIS framework improves classification results when combined with SVM.Unconstrained databases provide better results than that of constrained databases.We obtained 93.35 accuracy on Groups LFW cross-database test. In this paper, we propose a novel gender recognition framework based on a fuzzy inference system (FIS). Our main objective is to study the gain brought by FIS in presence of various visual sensors (e.g., hair, mustache, inner face). We use inner and outer facial features to extract input variables. First, we define the fuzzy statements and then we generate a knowledge base composed of a set of rules over the linguistic variables including hair volume, mustache and a vision-sensor. Hair volume and mustache information are obtained from Part Labels subset of Labeled Faces in the Wild (LFW) database and vision-sensor is obtained from a pixel-intensity based SVM+RBF classifier trained on different databases including Feret, Groups and GENKI-4K. Cross-database test experiments on LFW database showed that the proposed method provides better accuracy than optimized SVM+RBF only classification. We also showed that FIS increases the inter-class variability by decreasing false negatives (FN) and false positives (FP) using expert knowledge. Our experimental results yield an average accuracy of 93.35 using Groups LFW test, while the SVM performance baseline yields 91.25 accuracy. We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the maximum classification accuracy. This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA+LDA and SVM) and two performance measures (CCR and d') is provided over single- and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs.