Designing of Face Recognition System

Face Recognition is the most popular and trending technology in the present era. It is an effective way to provide vision to a machine for better interaction with humans. The way of living will be reflected if machines can read our faces. The face recognition system will move the world in a new dimension. It will be beneficial in many ways to find the identity and security. In this paper, a face recognition system is proposed for advanced applications such as access and security, payments, criminal identifications etc. The process of identification will be based on face recognition which is further divided into three steps: detection of face, extractions of the features and classification, and real time recognition. Detection of face is recognized as the essential step of our system. It is used to extract a face in a frame, which is based on the Viola-Jones object detection algorithm that uses AdaBoost classifier with Haar and LBP features. Local Binary Patterns (LBP) is utilized to extract the unique features of the face like eyes, nose, and mouth in the feature extraction phase. The facial image is correlated with the images available in the database for the classification. The system is implemented in Python using OpenCV library. Kivy is used to create a user interface and also to build executables for different platforms.

[1]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

[3]  Bin Xia,et al.  Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[4]  B. E. Reddy,et al.  Face Recognition Based on Texture Features using Local Ternary Patterns , 2015 .

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

[6]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[7]  P. Kanungo,et al.  A simple and fast automatic face colour identification for reducing the search space , 2017, 2017 2nd International Conference on Man and Machine Interfacing (MAMI).

[8]  Kari Pulli,et al.  Realtime Computer Vision with OpenCV , 2012, ACM Queue.

[9]  Takeshi Mita,et al.  Joint Haar-like features for face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.