Aprile 5, 2014

Gender Recognition Dataset


These videos and images have been used in order to test our methods for gender recognition in different scientific works.

UNISA-Public dataset is composed by 12 video sequences acquired in the University of Salerno in real environments. It was collected with the purpose of evaluating gender recognition algorithms in real life scenarios, since the images extracted from videos are surely more challenging than the ones provided by the classic datasets. We also provide the face images extracted with OpenCV and Matlab.

GENDER-FERET dataset is a balanced subset of the FERET dataset, adapted for gender recogntion purposes. It consists of 946 grayscale images, already divided in training set (237 m, 237 f) and test set (236 m, 236 f).

GENDER-COLOR-FERET dataset is a balanced subset of the COLOR-FERET dataset, adapted for gender recogntion purposes. In this case the images are coloured and the dataset is composed by 836 faces. The dataset is completely balanced, since both the training and the test set are composed of 209 male and 209 female faces.


If you use these datasets please cite:

  • Fusion of domain-specific and trainable features for gender recognition from face images.

    The popularity and the appeal of systems which are able to automatically determine the gender from face images is growing rapidly. Such a great interest arises from the wide variety of applications, especially in the fields of retail and video surveillance. In recent years there have been several attempts to address this challenge, but a definitive solution has not yet been found. In this paper we propose a novel approach that fuses domain-specific and trainable features to recognize the gender from face images. In particular, we use the SURF descriptors extracted from 51 facial landmarks related to eyes, nose and mouth as domain dependent features, and the COSFIRE filters as trainable features. The proposed approach turns out to be very robust with respect to the well known face variations, including different poses, expressions and illumination conditions. It achieves state-of-the-art recognition rates on the GENDER-FERET (94.7%) and on the LFW (99.4%) datasets, which are two of the most popular benchmarks for gender recognition. We further evaluated the method on a new dataset acquired in real scenarios, the UNISA-Public, recently made publicly available. It consists of 206 training (144 male, 62 female) and 200 test (139 male, 61 female) images that are acquired with a real-time indoor camera capturing people in regular walking motion. Such experiment has the aim to assess the capability of the algorithm to deal with face images extracted from videos, which are definitely more challenging than the still images available in the standard datasets. Also for this dataset we achieved a high recognition rate of 91.5%, that confirms the generalization capabilities of the proposed approach. Of the two types of features, the trainable COSFIRE filters are the most effective and, given their trainable character, they can be applied in any visual pattern recognition problem.

  • Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks.

    The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze the computational impact of the methods and the difficulties related to the processing of faces extracted from videos captured in the wild. We propose a novel face descriptor based on the gradient magnitudes of facial landmarks, which are points automatically extracted from the face contour, eyes, eyebrows, nose, mouth and chin. We evaluate the effectiveness and efficiency of the proposed approach on two new datasets, which we made available online and that consist of color face images and color video sequences acquired in real scenarios. The proposed approach is more efficient and effective than three commercial libraries.

  • Gender recognition from face images using a fusion of svm classifiers.

    The recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote. We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6 %, which outperforms the commercial products Face++ and Luxand.

  • Gender recognition from face images with trainable COSFIRE filters.

    Gender recognition from face images is an important application in the fields of security, retail advertising and marketing. We propose a novel descriptor based on COSFIRE filters for gender recognition. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest. We demonstrate the effectiveness of the proposed approach on a new dataset called GENDER-FERET with 474 training and 472 test samples and achieve an accuracy rate of 93.7%. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers. Furthermore, we perform another experiment by using the images of the Labeled Faces in the Wild (LFW) dataset to train our classifier and the test images of the GENDER-FERET dataset for evaluation. This experiment demonstrates the generalization ability of the proposed approach and it also outperforms two commercial libraries, namely Face++ and Luxand.


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