A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation

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• 作者：Carles Fern谩ndez (17)
  Ivan Huerta (18)
  Andrea Prati (18)
  
  17. Herta Security ; Pau Claris 165 4-B ; 08037 ; Barcelona ; Spain
  18. DPDCE ; University IUAV ; Santa Croce 1957 ; 30135 ; Venice ; Italy
  
• 关键词：Age estimation ; Support Vector Regression ; SVM ; Random Forest ; Multilayer Neural Networks ; Regularized Canonical Correlation Analysis ; CCA ; HOG
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8912
• 期：1
• 页码：133-144
• 全文大小：394 KB
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• 作者单位：Face and Facial Expression Recognition from Real World Videos
• 丛书名：978-3-319-13736-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The problem of automatic age estimation from facial images poses a great number of challenges: uncontrollable environment, insufficient and incomplete training data, strong person-specificity, and high within-range variance, among others. These difficulties have made researchers of the field propose complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification and regression schemes. We present a practical evaluation of four machine learning regression techniques from some of the most representative families in age estimation: kernel techniques, ensemble learning, neural networks, and projection algorithms. Additionally, we propose the use of simple HOG descriptors for robust age estimation, which achieve comparable performance to the state-of-the-art, without requiring piecewise facial alignment through tens of landmarks, nor fine-tuned and specific modeling of facial aging, nor additional demographic annotations such as gender or ethnicity. By using HOG descriptors, we discuss the benefits and drawbacks among the four learning algorithms. The accuracy and generalization of each regression technique is evaluated through cross-validation and cross-database validation over two large databases, MORPH and FRGC.