Age and gender recognition using informative features of various types

Fazl-Ersi, Ehsan; Mousa-Pasandi, Morteza; Laganière, Robert; Awad, Maher

doi:10.1109/icip.2014.7026190

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…For this reason, both results cannot be compared on equal terms. On the other hand, very recently, Fazl-Ersi et al (2014) obtained a slightly better result in the Gallagher database. However, this result is obtained by using a complex ensemble composed by several handcrafted features such as LBPs, Color Histograms and SIFT.…”

Section: Resultsmentioning

confidence: 98%

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Local Deep Neural Networks for gender recognition

Mansanet

Albiol

Paredes

2016

Pattern Recognition Letters

115

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Deep Learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such us the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed-forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.

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Section: Resultsmentioning

confidence: 98%

“…All the hidden units are ReLU, and the dropout technique has been also applied to the last hidden layer. Eidinger et al (2014) 88.60 LBP+CH+SIFT SVM Fazl-Ersi et al (2014) 91.59…”

Section: Resultsmentioning

confidence: 99%

Local Deep Neural Networks for gender recognition

Mansanet

Albiol

Paredes

2016

Pattern Recognition Letters

115

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“…Soon after, Fazl-Ersi et al [38] proposed to build an appearance-based model by fusing the features from Local Binary Patterns (LBP) [91], SIFT [81] and a color histogram (CH). In addition, they employed the feature selection method in [126] to extract the most informative features.…”

Section: Age Estimationmentioning

confidence: 99%

Real-time embedded age and gender classification in unconstrained video

Azarmehr

Laganière

Lee

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Recently, automatic demographic classification has found its way into embedded applications such as targeted advertising in mobile devices, and in-car warning systems for elderly drivers. In this thesis, we present a complete framework for video-based gender classification and age estimation which can perform accurately on embedded systems in real-time and under unconstrained conditions. We propose a segmental dimensionality reduction technique utilizing Enhanced Discriminant Analysis (EDA) to minimize the memory and computational requirements, and enable the implementation of these classifiers for resource-limited embedded systems which otherwise is not achievable using existing resource-intensive approaches. On a multi-resolution feature vector we have achieved up to 99.5% compression ratio for training data storage, and a maximum performance of 20 frames per second on an embedded Android platform.Also, we introduce several novel improvements such as face alignment using the nose, and an illumination normalization method for unconstrained environments using bilateral filtering. These improvements could help to suppress the textural noise, normalize the skin color, and rectify the face localization errors. A non-linear Support Vector Machine (SVM) classifier along with a discriminative demography-based classification strategy is exploited to improve both accuracy and performance of classification. We have performed several cross-database evaluations on different controlled and uncontrolled databases to assess the generalization capability of the classifiers. Our experiments demonstrated competitive accuracies compared to the resource-demanding state-of-the-art approaches.ii Acknowledgements I would like to take the opportunity to appreciate my supervisors, Prof. Robert Laganière, for his invaluable guidance, encouragement, and persistent support throughout the project and, Prof. WonSook Lee, for providing me with all the necessary facilities for research, and sharing expertise.

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“…In results, they achieved a ratio of 90% for gender classification. Fazl-Ersi et al [32] have compared between three methods that are LBP, Color Histogram (CH) and Scale-Invariant Feature Transform (SIFT) to recognize the gender. Moreover, they used SVM classifier to classify the features extracted from Gallagher facial images database [33].…”

Section: Introductionmentioning

confidence: 99%

Yerel İki̇li̇ Örüntü Ve Yönlü Gradyant Hi̇stogrami Kullanilarak Yüz Görüntüleri̇nden Ci̇nsi̇yet Tahmi̇ni̇

Khalifa¹,

Şengül

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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Gender prediction from facial images can be used in a large number of applications including human-computer interaction, customer information measurement, access control, etc. Furthermore, it can substantially effect on many fields, such as security systems, biometric authentication, medical imaging systems, demographic studies, content based searching, and surveillance system. In this study, we proposed to use Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) as the feature extractor and k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) as the classifier in order to predict the gender of the people from facial images. We tested the proposed method in FERET and UTD databases. We used leave-one-out approach as the cross validation technique. The results are promising.

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Age and gender recognition using informative features of various types

Cited by 24 publications

References 16 publications

Local Deep Neural Networks for gender recognition

Local Deep Neural Networks for gender recognition

Real-time embedded age and gender classification in unconstrained video

Yerel İki̇li̇ Örüntü Ve Yönlü Gradyant Hi̇stogrami Kullanilarak Yüz Görüntüleri̇nden Ci̇nsi̇yet Tahmi̇ni̇

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