Course Content

ABSTRACT

Facial feature detection is an active research topic in image understanding. In image understanding, facial feature parsing refers to the task of segmenting face images into different facial feature components, e.g., eyes, nose and mouth. The study of facial parsing is an attractive area due to the importance role it plays in multiple applications, including human identity recognition, animation, demographic analysis, facial image synthesis and faces image sketching, face swapping, face filters. All these applications ask for accurate segmentation that is robust to expression, pose and illumination variations. Facial feature extraction is the process of extracting face component features like eyes, nose, mouth, etc from human face image. Facial feature extraction is very much important for the initialization of processing techniques like face tracking, facial expression recognition or face recognition. Among all facial features, eye localization and detection is essential, from which locations of all other facial features are identified. We proposed a technique to detect different facial features like nose, mouth, jaw, eyes, eyebrows of a person facing webcam using python and opencv with the help of dlib package (library).

INTRODUCTION           

Human faces are of high significance in identifying the exact person based on the facial features. Several research works on facial study has been carried over decades in computer vision community. Face recognition is a challenging area in computer vision and pattern recognition because of its varied facial expressions, poses and illumination. Face recognition and facial expression recognition is very essential in areas like access management, human–computer communication, production control, e-learning, fatigue driving recognition and emotional robot. Face detection is an important part of and has been widely used in many fields, especially in tracking video signal, real-time monitoring and criminal investigation. The notion of the face detection initially came from face recognition. Face detection is the scanning and detection of face objects contained in an image using a certain strategy in a given image. Facial feature point’s location is the detection and extraction of points with key feature positions in face images, such as face contours, eyes, nose and mouth. We proposed a technique to detect different facial features like nose, mouth, jaw, eyes, eyebrows of a person facing webcam using python and opencv with the help of dlib package (library).

SYSTEM REQUIREMENTS

A. Hardware Requirement:-

•      System   :         Pentium IV 2.4 GHz.    

•      Hard Disk        :         500 GB.

•      Ram                 :         4 GB.

•      Any desktop / Laptop system with above configuration or higher level.

B. Software Requirements:-

•      Operating system       :         Windows XP / 7

•      Coding Language       :         Python

•      Interpreter                  :         Python 3.6

•      IDE                            :         Python IDLE

•      ML Libraries              :         Sklearn, numpy, opencv,dlib

Conclusion

The recognition of human faces plays an important role in many applications, for example in video surveillance and the management of facial image databases. Face detection and eyes extraction has an important role in many applications such as face recognition, facial expression analysis, security login etc. We proposed a technique to detect different facial features like nose, mouth, jaw, eyes, eyebrows of a person facing webcam using python and opencv with the help of dlib package (library).