What is pose-invariant face recognition?
Abstract. Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression.
What is pose invariance?
Abstract. Pose-invariant face recognition (PIFR) refers to the ability that recognizes face images with arbitrary pose variations.
Is there any difference between facial detection and facial recognition?
Face detection is a broader term than face recognition. Face detection just means that a system is able to identify that there is a human face present in an image or video. Face recognition can confirm identity. It is therefore used to control access to sensitive areas.
What is difference between detection and recognition?
Detection – The ability to detect if there is some ‘thing’ vs nothing. Recognition – The ability to recognize what type of thing it is (person, animal, car, etc.)
What are the results of a pose invariant framework?
A pose invariant framework. Robust results with respect to the identity, the head rotation and the variations of the face. 2. State of the art Face recognition technologies have attracted intensive interest due to their wide variety of applications in both industry and law-enforcement.
How to create a pose invariant 3D model?
The generation of a 3D face mesh from a single photo. A new approach combining numerical (3D mesh) and optical (correlation) methods for person identification. A pose invariant framework. Robust results with respect to the identity, the head rotation and the variations of the face.
How are 3D models used for face recognition?
The experiments are performed on the PHPID database enhanced with our 3D reconstructed faces of each person with an azimuth and an elevation ranging from −30° to +30°. The obtained results prove the robustness of our new method with 88.76% of good identification when the classic 2D approach (based on the VLC) obtains just 44.97%.
How to reconstruct faces in different poses?
This technique is based on an optimized 3D modeling allowing to reconstruct faces in different poses from a limited number of references (i.e. one image by class/person). Particularly, we propose to use an active shape model to detect a set of keypoints on the face necessary to deform our synthetic model with our optimized finite element method.