Proceedings ArticleDOI
Hierarchical and discriminative bag of features for face profile and ear based gender classification
Guangpeng Zhang,Yunhong Wang +1 more
TLDR
Hierarchical and discriminative bag of features technique is proposed to extract powerful features which are classified by support vector classification (SVC) with histogram intersection kernel and fusion of multi-modalities is performed at the score level based on Bayesian analysis to improve the accuracy.Abstract:
Gender is an important demographic attribute of human beings, automatic face based gender classification has promising applications in various fields. Previous methods mainly deal with frontal face images, which in many cases can not be easily obtained. In contrast, we concentrate on gender classification based on face profiles and ear images in this paper. Hierarchical and discriminative bag of features technique is proposed to extract powerful features which are classified by support vector classification (SVC) with histogram intersection kernel. With the output of SVC, fusion of multi-modalities is performed at the score level based on Bayesian analysis to improve the accuracy. Experiments are conducted using texture images of the UND biometrics data sets Collection F, and average classification accuracy of 97.65% is achieved, which is comparable to the state of the art. Our work can be used in cooperate with existing frontal face based methods for accurate multi-view gender classification.read more
Citations
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Journal ArticleDOI
Demographic Estimation from Face Images: Human vs. Machine Performance
TL;DR: A generic framework for automatic demographic (age, gender and race) estimation is presented and crowdsourcing is used to study the human perception ability of estimating demographics from face images.
Journal ArticleDOI
Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers
TL;DR: This survey first presents biometric demographic analysis from the standpoint of human perception, then provides a comprehensive overview of state-of-the-art advances in automated estimation from both academia and industry.
Journal ArticleDOI
A review of facial gender recognition
TL;DR: There is still much work that can be done to improve the robustness of gender recognition under real-life environments, and the datasets used for evaluation of gender classification performance are appraised.
Journal ArticleDOI
Automatic Ear Landmark Localization, Segmentation, and Pose Classification in Range Images
TL;DR: This paper is the first to present automatic landmark localization of 3-D ears extracted from facial scans with significant pose variations, and an effective and efficient system of ear landmark localization, ear detection, and pose classification based on 3- D ears captured under large yaw variations.
Proceedings ArticleDOI
Gender classification using automatically detected and aligned 3D ear range data
TL;DR: The first attempt for gender classification from 3D ear data is demonstrated and it is observed that the use of Histogram of Indexed Shapes (HIS) feature along with Support Vector Machine (SVM) yields an average classification accuracy of 92.94%.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Proceedings ArticleDOI
A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei,Pietro Perona +1 more
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.