Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Age classification from facial images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
A Method for Estimating and Modeling Age and Gender using Facial Image Processing
VSMM '01 Proceedings of the Seventh International Conference on Virtual Systems and Multimedia (VSMM'01)
Aging of Orbicularis Muscle in Virtual Human Faces
IV '03 Proceedings of the Seventh International Conference on Information Visualization
Active Appearance Models Revisited
International Journal of Computer Vision
A tutorial on support vector regression
Statistics and Computing
A Generative Sketch Model for Human Hair Analysis and Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Age Estimation Using AAM and Local Facial Features
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
A Compositional and Dynamic Model for Face Aging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Age estimation using active appearance models and support vector machine regression
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy output support vector machines for classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Human Age Estimation With Regression on Discriminative Aging Manifold
IEEE Transactions on Multimedia
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
IEEE Transactions on Image Processing
Image and Vision Computing
Biometric cryptosystem based on discretized fingerprint texture descriptors
Expert Systems with Applications: An International Journal
Age classification based on back-propagation network
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed of an age group classification (e.g. the class of less than 20 years old, the class of 20-39 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted in the previous works. Consequently, we propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways, compared to the previous works. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Thirdly, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.