AN EFFICIENT APPROACH FOR FACE DETECTION AND FACIAL FEATURE LOCATION USING PRUNE-AND-SEARCH TECHNIQUE

2004 ◽  
Vol 18 (06) ◽  
pp. 987-1005 ◽  
Author(s):  
CHIN-CHEN CHANG ◽  
YUAN-HUI YU
Keyword(s):  
Face Detection ◽  
Facial Feature ◽  
Facial Features ◽  
Base Line ◽  
Human Face ◽  
Feature Location ◽  
Search Technique ◽  
Efficient Approach ◽  
Human Face Detection ◽  
Geometric Knowledge

This paper proposes an efficient approach for human face detection and exact facial features location in a head-and-shoulder image. This method searches for the eye pair candidate as a base line by using the characteristic of the high intensity contrast between the iris and the sclera. To discover other facial features, the algorithm uses geometric knowledge of the human face based on the obtained eye pair candidate. The human face is finally verified with these unclosed facial features. Due to the merits of applying the Prune-and-Search and simple filtering techniques, we have shown that the proposed method indeed achieves very promising performance of face detection and facial feature location.

A SYSTEM FOR HUMAN FACE AND FACIAL FEATURE LOCATION

2003 ◽  
Vol 03 (03) ◽  
pp. 461-479 ◽  
Author(s):  
JUN MIAO ◽  
HONG LIU ◽  
WEN GAO ◽  
HONGMING ZHANG ◽  
GANG DENG ◽  
...  
Keyword(s):  
Face Detection ◽  
Facial Feature ◽  
Center Of Gravity ◽  
Facial Features ◽  
Detection Accuracy ◽  
Human Face ◽  
Feature Location ◽  
Illumination Compensation ◽  
Human Faces ◽  
Accuracy Rates

This paper presents an implementation of a system designed for the location of human faces and facial features such as pupils, eyes, nose and mouth. The kernel of the system is an integration of several algorithms, such as the human face center-of-gravity template, illumination compensation, and so on. A false-face removal algorithm is proposed in this paper specially for the distinguishing of cartoon faces from true faces. The testing experiments of the system have produced quite good results, with the average detection accuracy rates for face detection and facial feature location being 97.8% and 87.5% respectively.

Analysis of Face Detection and Feature Location Application on Intelligent Character Design Situations

2013 ◽  
Vol 756-759 ◽  
pp. 3962-3966
Author(s):  
Hong Yan Li
Keyword(s):  
Face Detection ◽  
Facial Feature ◽  
Design System ◽  
Human Face ◽  
Feature Location ◽  
Facial Image ◽  
Threshold Method ◽  
Adaboost Algorithm ◽  
Character Design

Process of face detection and face feature location on sequence images is analyzed in this paper. Firstly, designer image is captured. Secondly, color threshold method is used to detect whether the image containing human face. thirdly, after above detection, if a human face existing, the Adaboost algorithm based on improved weight update is used to locate facial feature parts. Location on facial feature parts is premise of designing facial image and foundation for realization of intelligent character design system.

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.

scholarly journals Normal and Abnormal Human Face Detection Based on DCT and FFT Techniques - A Proposed Method

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta ◽  
Vishal Goyal ◽  
Payal Bose
Keyword(s):  
Face Detection ◽  
Fourier Transformation ◽  
Input Image ◽  
The Other ◽  
Fast Fourier Transformation ◽  
Length Variation ◽  
Human Face ◽  
Discrete Cosine Transformation ◽  
Cosine Transformation ◽  
Human Face Detection

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

scholarly journals Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

HUMAN FACE IMAGE PROCESSING TECHNIQUES

2001 ◽  
Vol 01 (02) ◽  
pp. 197-215 ◽  
Author(s):  
HONG YAN
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Facial Feature ◽  
Face Image ◽  
Computer Algorithms ◽  
Human Face ◽  
Feature Location ◽  
Image Processing Techniques ◽  
Face Image Processing ◽  
Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

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