A New Discrete Cosine Transform on Face Recognition through Histograms for an Optimized Compression

Sufyanu, Zahraddeen; Mohamad, Fatma Susilawati; Yusuf, Abdulganiyu Abdu

doi:10.3923/rjit.2015.101.111

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Once the DCT is enforced on input image, the DCT coefficients are followed in zig-zag manner in order to convert image matrix into feature vector. Only a few DCT coefficients are enough for reducing redundancy and recovering original image from the selected coefficients [24].…”

Section: Discrete Cosine Transform (Dct)mentioning

confidence: 99%

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Saini¹,

Malhotra²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Among various types of biometric techniques it is the face recognition that gains huge popularity in the last few decades due to its vast applicationsand more user friendly nature.Face Recognition is the procedure of identification and verification of a person's identity using his face. Main steps involved in a FR System are Face Detection, Feature Extraction, Feature Selection and Recognition. Feature Extraction and Feature Selection are the two main phases to be focused on in order to get a good FR System. In this paper, we have carried out anextensive literature review on face recognition focusing on feature extraction and feature selection phases a bit more.

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Section: Discrete Cosine Transform (Dct)mentioning

confidence: 99%

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Saini¹,

Malhotra²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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“…[17] This paper proposed algorithm to extract local information depending on block-based discrete cosine transform. [18] it used DCT as a feature extractor from the images that suffer from poor illumination. In [19] (DCT) is employed to address the human face images in difficult lighting conditions.…”

Section: Previous Workmentioning

confidence: 99%

A Case Study on Various Preprocessing Methods and their Impact on Face Recognition using Random Forest

Algharib¹,

Rehman²

2017

IJCA

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The Random forest is a well-known powerful classifier, that used to classify a wide range of patterns in our daily life for different purposes, it enters into many fields such as images and objects classification. In this paper, we studied the impact of a five-common preprocessing method in face recognition on the random forest performance, the study included applying five different pre-processing methods (Single Scale Retinex, Discreet Cosine Transform, wavelet Denoising, Gradient faces, and the method proposed by tan and et Known as pp chain or TT), each one has applied separately with a general random forest as a classifier, we computed the error rate for each method. The study was conducted on a face recognition system under occlusion and illumination variation. All experiments were done using MATLAB and Extended Yale B database.

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A New Discrete Cosine Transform on Face Recognition through Histograms for an Optimized Compression

Cited by 2 publications

References 8 publications

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

A Case Study on Various Preprocessing Methods and their Impact on Face Recognition using Random Forest

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