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Face detection is important part of surveillance systems and it has been widely used in computer vision and image processing. Face detection is also first step of the facial feature extraction. Facial feature extraction is a topic that has been focused on by many researchers in computer science, psychology, medicine and related fields and has become increasingly important in recent years. With the help of facial features, machine learning algorithms can estimate ages and classify genders of people. In this paper, face detection, facial feature extraction, age estimation and gender classification are presented. Firstly, face detection and extraction of facial features like eyes, eyebrows, mouth and nose are presented. Secondly, age estimation and gender classification based on the extracted facial features are explained. Experimental results prove that face detection algorithm efficiently detects human faces and facial feature algorithm accurately locates eyes, eyebrows, mouth and nose. Experimental results also show that, based on the extracted facial features, convolutional neural network architecture estimates ages of the people and classifies their gender.

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