Human face detection with neural networks and the DIRECT algorithm

2008 ◽  
Vol 12 (1-2) ◽  
pp. 112-115 ◽  
Author(s):  
Yau-Zen Chang ◽  
Kao-Ting Hung ◽  
Shih-Tseng Lee
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Direct Algorithm ◽  
Human Face ◽  
Human Face Detection

scholarly journals Face Detection Using Backpropagation Neural Networks

2007 ◽  
Vol 1 (2) ◽  
Author(s):  
Anifuddin Azis ◽  
Muhamad Haikal
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Research Effort ◽  
Back Propagation ◽  
Identification System ◽  
Human Face ◽  
Edge Deletion ◽  
Backpropagation Neural Networks ◽  
Human Face Detection ◽  
Human Faces

AbstractThis paper summarizes a research effort in human face detection. A system to locate human faces in images, especially when used as a front-end for a human face identification system, could have many applications in the law enforcement and security professions. The approach presented here is a hybrid system using an edge deletion preprocessor and back-propagation neural networks. The method proposed successfully detected multiple faces. The results obtained are reported along with a discussion for improving the system.Keywords: Backpropagation neural networks, Edge Detection

scholarly journals Human Face Detection In Visual Scenes Using Neural Networks

2002 ◽  
Vol 122 (6) ◽  
pp. 995-1000 ◽  
Author(s):  
Stephen Karungaru ◽  
Minoru Fukumi ◽  
Norio Akamatsu
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Human Face ◽  
Human Face Detection ◽  
Visual Scenes

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.

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.

Fast hierarchical knowledge-based approach for human face detection in color images

2001 ◽  
Author(s):  
Jun Jiang ◽  
Jie Gong ◽  
Guilin Zhang ◽  
Ruolan Hu
Keyword(s):  
Face Detection ◽  
Color Images ◽  
Human Face ◽  
Knowledge Based ◽  
Human Face Detection
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