Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

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What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.

Vision-based Human Gender Recognition: A Survey

In many classification problems such as electroencephalogram (EEG) classification and image classification, the input features are naturally represented as matrices rather than vectors or scalars. In general, the structure information of the original feature matrix is useful and informative for data analysis tasks such as classification. One typical structure information is the correlation between columns or rows in the feature matrix. To leverage this kind of structure information, we propose a new classification method that we call support matrix machine (SMM). Specifically, SMM is defined as a hinge loss plus a so-called spectral elastic net penalty which is a spectral extension of the conventional elastic net over a matrix. The spectral elastic net enjoys a property of grouping effect, i.e., strongly correlated columns or rows tend to be selected altogether or not. Since the optimization problem for SMM is convex, this encourages us to devise an alternating direction method of multipliers (ADMM) algorithm for solving the problem. Experimental results on EEG and image classification data show that our model is more robust and efficient than the state-of-the-art methods.

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Support Matrix Machines

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition.

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Gender classification: a convolutional neural network approach

Automated gender estimation has numerous applications, including video surveillance, human–computer interaction, anonymous customized advertisement, and image retrieval. Most commonly, the underlying algorithms analyze the facial appearance for clues of gender. In this paper, we propose a novel method for gender estimation, which exploits dynamic features gleaned from smiles and we proceed to show that: a) facial dynamics incorporate clues for gender dimorphism and b) while for adult individuals appearance features are more accurate than dynamic features, for subjects under 18 years facial dynamics can outperform appearance features. In addition, we fuse proposed dynamics-based approach with state-of-the-art appearance-based algorithms, predominantly improving performance of the latter. Results show that smile-dynamics include pertinent and complementary to appearance gender information.

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Gender Estimation Based on Smile-Dynamics

The study presents an efficient gender classification technique. The gender of a facial image is the most prominent feature, and improvement in the existing gender classification methods will result in the high performance of the face retrieval and classification methods for large repositories. In this paper a new efficient gender classification method is proposed. First, the face part of the image is segmented using Viola and Jones face detection technique which excludes unwanted area from the image, so reducing image size. Histogram equalization is performed to normalize the illumination effect. Discrete Cosine Transform (DCT) is employed for feature extraction and sorting the features with high variance. K-nearest neighbor classifier (KNN) is used for classification. The face images used in this study were obtained from the Stanford university medical student (SUMS) frontal facial images database. The experimental results on the SUMS face database indicate that the proposed approach achieves as high as 99.3% gender classification accuracy.

http://www.wseas.us/e-library/conferences/2010/Iasi/NNECFS/NNECFS-08.pdf

Anab Batool

Papers

Feature selection for efficient gender classification