An experimental comparison of gender classification methods

Mäkinen, Erkki; Raisamo, Roope

doi:10.1016/j.patrec.2008.03.016

Cited by 173 publications

(142 citation statements)

References 17 publications

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“…We used fa-and fb-subsets from the FERET database by removing duplicate images of the same subject; at the end there are 450 facial images for both females and males. For the MORPH database, which is not used in [1], we collect 8,033 female and 47,810 male images; there are totally 418 subjects of age ranging from 18-69 and of races of Caucasian, AfricanAmerican and Asian. To the best of our knowledge, we are the first tackling the gender identification problem over such variations with wide range of age and races.…”

Section: Resultsmentioning

confidence: 99%

“…Previous studies on gender identification have relied on careful alignment of a facial image into a standard template. The studies in [1], [2] conduct experiments on many combination of the state-of-the-art face alignment and pattern classification methods for gender identification. The authors found that the classification rates increase with the accuracy of face alignment because such alignment could reduce the variability during building the model in the training phase.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Gender from Unaligned Facial Images by Set Classification

Chu

Huang

Chen

2010

2010 20th International Conference on Pattern Recognition

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Abstract-Rough face alignments lead to suboptimal performance of face identification systems. In this study, we present a novel approach for identifying genders from facial images without proper face alignments. Instead of using only one input for test, we generate an image set by randomly cropping out a set of image patches from a neighborhood of the face detection region. Each image set is represented as a subspace and compared with other image sets by measuring the canonical correlation between two associated subspaces. By finding an optimal discriminative transformation for all training subspaces, the proposed approach with unaligned facial images is shown to outperform the state-of-the-art methods with face alignment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Gender from Unaligned Facial Images by Set Classification

Chu

Huang

Chen

2010

2010 20th International Conference on Pattern Recognition

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“…This kind of approach has been shown to achieve high performance rates [4,1], but a strong preprocessing stage (normalization, alignment, scaling and/or histogram equalization) is needed. Based on the state of the art, we normalize and align the images using the eye positions, and rescale them to a resolution of 24 × 24 pixels.…”

Section: Classification Methods Using Normalized Imagesmentioning

confidence: 99%

“…Automatic gender classification of face images is an area of growing interest with multiple applications such as demographic data collection or facial expression recognition (for a recent review see [1]). In most practical applications, the properties of the images that will be used in the exploitation phase (illumination, pose, scale, orientation, occlusions, etc.)…”

Section: Introductionmentioning

confidence: 99%

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A comparison of techniques for robust gender recognition

Rojas-Bello

Lago-Fernández

Martínez-Muñoz

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Esta es la versión de autor del congreso publicado en: This is an author produced version of a paper published in: ABSTRACTAutomatic gender classification of face images is an area of growing interest with multiple applications. Appropriate classifiers should be robust against variations such as illumination, scale and orientation that occur in real world applications. This can be achieved by normalizing the images in order to reduce those variations (alignment, re-scaling, histogram-equalization, etc.), or by extracting features from the original images which are invariant respect to those variations. In this work we perform a robust comparison of eight different classifiers across 100 random partitions of a set of frontal face images. Four of them are state-of-the-art methods in automatic gender classification that use image normalization (SVMs, Neural Networks, ADABOOST and PCA+LDA). The other four strategies use invariant features extracted by SIFT (BOW, Evidence Random Trees, NBNN and Voted Nearest-Neighbor). The best strategies are SVM using normalized images and NBNN, the latter having the advantage that no strong image pre-processing is needed.

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Face detection based on multilayer feed‐forward neural network and Haar features

2018

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Summary Fast and accurate detection of a facial data is crucial for both face and facial expression recognition systems. These systems include internet protocol video surveillance systems, crime scene photographs systems, and criminals' databases. The aim for this study is both improvement of accuracy and speed. The salient facial features are extracted through Haar techniques. The sizes of the images are reduced by Bessel down‐sampling algorithm. This method preserved the details and perceptual quality of the original image. Then, image normalization was done by anisotropic smoothing. Multilayer feed‐forward neural network with a back‐propagation algorithm was used as classifier. A detection accuracy of 98.5% with acceptable false positives was registered with test sets from FDDB, CMU‐MIT, and Champions databases. The speed of execution was also promising. An evaluation of the proposed method with other popular detectors on the FDDB set shows great improvement.

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An experimental comparison of gender classification methods

Cited by 173 publications

References 17 publications

Identifying Gender from Unaligned Facial Images by Set Classification

Identifying Gender from Unaligned Facial Images by Set Classification

A comparison of techniques for robust gender recognition

Face detection based on multilayer feed‐forward neural network and Haar features

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