Abstract—Face detection technique is used for face authentication and verification and face detection is a front part of face recognition. It is used in many fields such as authentication security, video surveillance and human interaction system. In this paper we have collected data of 400 faces from school students in Muzaffarabad, Azad Kashmir. Besides, 50 non-faces are also collected. Both faces and non-faces are preprocessed using Background Elimination, Noise Reduction, Width Normalization and Thinning. After the preprocessing, we have extracted features from 400 faces and 50 non-faces including Geometric Features such as Image Cropping, Vertical/Horizontal Projection, Global Features such as Aspect Ratio, Normalized Area of Faces and Non-faces, Center of Gravity, Slope of Line joining the center of Gravity and texture features. Finally, we have applied Machine Learning Methods such as Bayes, Function, Lazy, Meta, Misc, Rules and Tree to classify the faces and non-faces using 10 fold cross validation. HyperPipes gives an overall higher accuracy of 99.8%, while ADTree, LWL and LogiBoost gives accuracy of more than 99%. The average AUC of ROC value was calculated as 96.08%.

Index Terms—classification, receiver operating curve, feature extraction, preprocessing, cross validation

Cite: Lal Hussain, Wajid Aziz, Zaki H. Kazmi, and Imtiaz A. Awan, "Classification of Human Faces and Non Faces Using Machine Learning Techniques," International Journal of Electronics and Electrical Engineering, Vol. 2, No. 2, pp. 116-123, June 2014. doi: 10.12720/ijeee.2.2.116-123