Application for video analysis based on machine learning and computer vision algorithms

An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features and support vector machines are proposed. All the stages are united into a single system of audience analysis. The proposed software complex can find its applications in different areas, from digital signage and video surveillance to automatic systems of accident prevention and intelligent human-computer interfaces.

[2]  Roope Raisamo,et al.  An experimental comparison of gender classification methods , 2008, Pattern Recognit. Lett..

[3]  Kang Ryoung Park,et al.  A comparative study of local feature extraction for age estimation , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[5]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[6]  Xiaolong Wang,et al.  A study on human age estimation under facial expression changes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Yun Fu,et al.  Age Synthesis and Estimation via Faces: A Survey , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Shinichi Tamura,et al.  Male/female identification from 8×6 very low resolution face images by neural network , 1996, Pattern Recognit..

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Rama Chellappa,et al.  A hierarchical approach for human age estimation , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Guodong Guo,et al.  Human age estimation: What is the influence across race and gender? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[13]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[14]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[17]  Wei-Yun Yau,et al.  Effects of facial alignment for age estimation , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[18]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[19]  Zhen Li,et al.  A robust framework for multiview age estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[20]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..