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

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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Pharmacy Impact for Distinguishing Normal Face from Abnormal Face Due to COVID- 19

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i43a32460 ◽

2021 ◽

pp. 15-23

Author(s):

Payal Bose ◽

Shawni Dutta ◽

Vishal Goyal ◽

Samir K. Bandyopadhyay

Keyword(s):

Health Risks ◽

Fourier Transformation ◽

Research Work ◽

Input Image ◽

Fast Fourier Transformation ◽

Length Variation ◽

Human Face ◽

Discrete Cosine Transformation ◽

Cosine Transformation ◽

Deadly Disease

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. In the recent scenario, the entire globe is facing enormous health risks occurred due to Covid-19. To fight against this deadly disease, consumption of drugs is essential. Consumption of drugs may provide some abnormalities to human face. 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. To assess these human face abnormalities, the application of computer vision is favoured in this study. This work analyses an input image of human’s frontal face and performs a segregation method to separate the abnormal faces. In this research work, a method has been proposed that can detect normal or abnormal faces from a frontal input image due to COVID-19. This method has used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

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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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Human Face Detection in Color Images

10.32920/ryerson.14665272 ◽

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.

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