Face Recognition using Modified Triangle Method

Alfredo Bayu Satriya, Siti Agustini

Abstract


Abstrak. Pengenalan wajah diperlukan untuk beberapa aplikasi seperti siatem keamanan. Pada penelitian ini, peneliti memberikan metode triangle yang baru untuk deteksi dan pengenalan wajah dari wajah seseorang yang diaplikasikan untuk sistem keamanan. Metode ini berdasarkan deteksi warna kulit untuk mendeteksi wajah manusia dan mengenali wajah tersebut menggunakan metode modified triangle. Yang dilakukan pertama kali adalah pemrosesan gambar dimana semua gambar dijadikan dalam resolusi yang sama. Kemudian, metode ini akan melakukan skin detection berdasarkan warna kulit dari gambar, non-face object akan dihapus oleh morphological method untuk mendapatkan hanya gambar wajah saja, segmentasi wajah, deteksi features point dari wajah (mata kanan, mata kiri, dan mulut) dan kemudia dihitung Euclidean distance diantara features face. Jarak antara eatures face akan dibandingkan dengan database untuk proses pengenalan wajah. Dari hasil penelitian menunjukkan bahwa metode ini dapat mencapai tingkat keberhasilan 90% dalam deteksi wajah dan 50% untuk pengenalan wajah.

Kata kunci: deteksi wajah, pengenalan wajah, triangle method

Abstract. Human face recognition is required for several applications such as security system. In this paper, we propose a new triangle method for detection and recognition of the human face which is applied for security system. We use the skin colour based detection to detect the human face and recognize the selected face by modified triangle method. First, we do image pre-processing which include change all picture in same resolution. Next, this method will do skin detection based skin colour from the image, nonface object removal by morphological method for getting the face of image only, face segmentation, detect features point of face (right eye, left eye, and mouth) and then compute Euclidean distance between features face. The distance between features face will be compared with database for face recognition. Experimental result shows that our method can achieve 90% success rate for face detection and 50% for face recognition.

Keywords : face detection, face recognition, triangle method


Full Text:

PDF

References


H. Y. Chen, C. M. Fu, and C. L. Huang. 2008. Hybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition, Pattern Recognition, pp. 1173–1185

L. Gao and Y. Xu. 2012. Face orientation recognition based on multiple facial feature triangles, International Conference on Control Engineering and Communication Technology

J. Harguess and J. K. Aggarwal. 2009. A Case for the Average-Half-Face in 2D and 3D for Face Recognition, Computer Vision and Pattern Recognition Workshops. CVPR Workshops 2009. IEEE Computer Society Conference on Pattern Recognition

Y. Z. Jie Yang, Xufeng Ling and Z. Zheng. 2008. A Face Detection and Recognition System in Color Image Series, Mathematics and Computers in Simulation, pp. 531–539

H. Lu, K.N. Plataniotis and A.N. Venetsanopoulos. 2006. MPCA: Multilinear Principal Component Analysis of Tensor Objects, IEEE 2006 Biometrics Symposium ,Sept.

C. Lin, K.-C. Fan. 2000. Human Face Detection Using Geometric Triangle Relationship, Proceedings 15 Th International Conference On Pattern Recognition, Sept.

C. Ming and C. Yisong. 2010. A hardware/software co-design of a face detection algorithm based on FPGA, International Conference on Measuring Technology and Mechatronics Automation, pp. 109–112

S. Phimoltares, C. Lursinsap, and K. Chamnongthai. 2007. FaceDetection and Facial Feature Localization without Considering the Appearance of Image Context, Image and Vision Computing,pp. 741–753

J. Tang and J. Zhang. 2009. Eye Tracking Based on Grey Prediction, First International Workshop on Education Technology and Computer Science, pp. 861–864

M. Turk and A.Pentland. 1991. Face Recognition using Eigen Faces, Computer Vision and Pattern Recognition, pp. 586–591

R. Vranceanu, R. Condorovici, C. Patrascu, F. Coleca, and L. Florea. 2011. Robust Detection and Tracking of Salient Face Features in Color Video Frames, 10th International Symposium on Signals, Circuits and Systems (ISSCS), 2011.

X. Wu Zhang, D. Duan, Ling-yan Liang, and W. liang Xia. 2009. A Novel Method of Face Detection Based on Fusing YCbCr and HIS Color Space, Proceedings of the IEEE International Conference on Automation and Logistics, pp. 831–835.


Refbacks

  • There are currently no refbacks.