The Facial Features Extraction for Face Recognition Based on Geometrical Approach

2006 ◽  
Author(s):  
Basavaraj Amarapur ◽  
Nagaraj Patil
Keyword(s):  
Face Recognition ◽  
Features Extraction ◽  
Facial Features ◽  
Geometrical Approach

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Pre filtering techniques for face recognition based on edge detection algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 213
Author(s):  
Sheela Rani ◽  
Vuyyuru Tejaswi ◽  
Bonthu Rohitha ◽  
Bhimavarapu Akhil
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Facial Features ◽  
Human Face ◽  
Video Frames ◽  
Similar Idea ◽  
The Individual ◽  
The Given ◽  
Interesting Area

Recognition of face has been turned out to be the most important and interesting area in research. A face recognition framework is a PC application that is apt for recognizing or confirming the presence of human face from a computerized picture, from the video frames etc. One of the approaches to do this is by matching the chosen facial features with the pictures in the database. It is normally utilized as a part of security frameworks and can be implemented in different biometrics, for example, unique finger impression or eye iris acknowledgment frameworks. A picture is a mix of edges. The curved line potions where the brightness of the image change intensely are known as edges. We utilize a similar idea in the field of face-detection, the force of facial colours are utilized as a consistent value. Face recognition includes examination of a picture with a database of stored faces keeping in mind the end goal to recognize the individual in the given input picture. The entire procedure covers in three phases face detection, feature extraction and recognition and different strategies are required according to the specified requirements.

Use of Face Information Varies Systematically From Developmental Prosopagnosics to Super-Recognizers

2018 ◽  
Vol 30 (2) ◽  
pp. 300-308 ◽  
Author(s):  
Jessica Tardif ◽  
Xavier Morin Duchesne ◽  
Sarah Cohan ◽  
Jessica Royer ◽  
Caroline Blais ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Scales ◽  
Face Identification ◽  
Facial Features ◽  
Strongly Correlated ◽  
Identification Task ◽  
Spatial Frequencies ◽  
Recognition Ability ◽  
Neurologically Typical

Face-recognition abilities differ largely in the neurologically typical population. We examined how the use of information varies with face-recognition ability from developmental prosopagnosics to super-recognizers. Specifically, we investigated the use of facial features at different spatial scales in 112 individuals, including 5 developmental prosopagnosics and 8 super-recognizers, during an online famous-face-identification task using the bubbles method. We discovered that viewing of the eyes and mouth to identify faces at relatively high spatial frequencies is strongly correlated with face-recognition ability, evaluated from two independent measures. We also showed that the abilities of developmental prosopagnosics and super-recognizers are explained by a model that predicts face-recognition ability from the use of information built solely from participants with intermediate face-recognition abilities ( n = 99). This supports the hypothesis that the use of information varies quantitatively from developmental prosopagnosics to super-recognizers as a function of face-recognition ability.

Features Extraction for Robust Face Recognition Using GLCM and CS-LBP

2021 ◽  
pp. 175-191
Author(s):  
Abeer D. Salman ◽  
Mohammed Ahmed Talab ◽  
Ruqayah R. Al‐Dahhan
Keyword(s):  
Face Recognition ◽  
Features Extraction ◽  
Robust Face Recognition

Biometrical Processing of Faces in Security and Forensics

2010 ◽  
pp. 79-103
Author(s):  
Pawel T. Puslecki
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Face Processing ◽  
Facial Features ◽  
Comprehensive Description ◽  
Facial Processing ◽  
Human Faces

The aim of this chapter is the overall and comprehensive description of the machine face processing issue and presentation of its usefulness in security and forensic applications. The chapter overviews the methods of face processing as the field deriving from various disciplines. After a brief introduction to the field, the conclusions concerning human processing of faces that have been drawn by the psychology researchers and neuroscientists are described. Then the most important tasks related to the computer facial processing are shown: face detection, face recognition and processing of facial features, and the main strategies as well as the methods applied in the related fields are presented. Finally, the applications of digital biometrical processing of human faces are presented.

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