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

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

HUMAN FACE DETECTION USING SILHOUETTES

1995 ◽  
Vol 09 (06) ◽  
pp. 845-867 ◽  
Author(s):  
ASHOK SAMAL ◽  
PRASANA A. IYENGAR
Keyword(s):  
Principal Component Analysis ◽  
Face Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Human Face ◽  
Secure Systems ◽  
Human Face Detection ◽  
Intensity Images ◽  
Human Faces

Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.

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 Real-Time Face Detection and Tracking

2020 ◽  
Vol 8 (6) ◽  
pp. 5116-5118
Keyword(s):  
Face Detection ◽  
Object Identification ◽  
Human Face ◽  
Detection And Tracking ◽  
The People ◽  
Time Period ◽  
Human Face Detection ◽  
Python Programming ◽  
Human Faces ◽  
Image Difference

Face detection, face tracking, and Object identification is the first process in applications such as face detection-based attendance marking system, video surveillance, and tracking of human faces in case of emergency. The main objective of our project is to detect and track the moving human faces with a permanently placed fixed camera. We propose a general moving face detection and tracking system.Our project mainly focuses on the moving human face detection in a situation, let us say, the people moving together are meeting with each other and are detected as the people as long as they stay in the situation. This can be done with the help of an Image Difference Algorithm with the python programming language support, and also that the time period for each and every frame can be calculated.

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.

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