Human Face Detection and Tracking Using RetinaFace Network for Surveillance Systems

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.

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Human face detection and tracking based on supervised learning

10.1117/12.749583 ◽

2007 ◽

Cited By ~ 1

Author(s):

Min Luo ◽

Xiaohui Duan ◽

Shiwen Zhu ◽

Zheng Song ◽

Chaohui Zhan

Keyword(s):

Supervised Learning ◽

Face Detection ◽

Human Face ◽

Detection And Tracking ◽

Human Face Detection

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003551 ◽

2004 ◽

Vol 18 (06) ◽

pp. 987-1005 ◽

Cited By ~ 2

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.

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Object-based human face detection

2000 Digest of Technical Papers. International Conference on Consumer Electronics. Nineteenth in the Series (Cat. No.00CH37102) ◽

10.1109/icce.2000.854680 ◽

2002 ◽

Author(s):

Heung Soo Kim ◽

Eun Yi Kim ◽

Sang Won Hwang ◽

Hang Joon Kim

Keyword(s):

Face Detection ◽

Human Face ◽

Object Based ◽

Human Face Detection

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Human Face Detection Using New High Speed Modular Neural Networks

Artificial Neural Networks: Biological Inspirations – ICANN 2005 - Lecture Notes in Computer Science ◽

10.1007/11550822_85 ◽

2005 ◽

pp. 543-550

Author(s):

Hazem M. El-Bakry

Keyword(s):

Neural Networks ◽

Face Detection ◽

High Speed ◽

Human Face ◽

Modular Neural Networks ◽

Human Face Detection

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

10.20944/preprints202107.0570.v1 ◽

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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