Face Recognition Based on DCT and 2DLDA

Yin, Hongpeng; Fu, Ping; Qiao, Jiaqing

doi:10.1109/icicic.2007.302

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Most of studies that focus on feature extraction using DCT only use 2D-DCT and is applied to images (Kharat & Dudul, 2008;Kim & Kim, 2007;Tachaphetpiboon & Amornraksa, 2007;Yin, Fu, & Qiao, 2007). However, it is not commonly used for signals like as gait that is one dimensional.…”

Section: Discussionmentioning

confidence: 99%

Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Banaie¹,

Pooyan²,

Mikaili³

2011

Expert Systems with Applications

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Section: Discussionmentioning

confidence: 99%

Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Banaie¹,

Pooyan²,

Mikaili³

2011

Expert Systems with Applications

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“…The definition of the two -dimensional DCT for an input image A and output image B is where M and N are the row and column size of A, respectively. If you apply the DCT to real data, the result is also real [4]. The DCT decomposes a signal into its elementary frequency components.…”

Section: Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Novel Feature Extraction Technique for Indian Sign Language Recognition using Energy Compaction of Cosine Transform

Thepade¹,

Phatak²,

Naglot³

et al. 2017

IJCA

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Sign language is the basic medium of communication for the deaf and dumb people. It has evolved as one of the major areas of research and study in Computer Vision. In this paper we display the importance of Indian Sign Language and proposed techniques for feature extraction and their efficient results. Indian Sign Language has a total of 26 alphabets using either one hand or both hands to show the sign. With the help of energy compaction using discrete cosine transform, maximum energy is packed into lows frequency region. In order to ensure efficient feature extraction and enabling feature vector size to be as small as possible, this paper proposes a novel technique to perform feature extraction and obtain high efficiency. Two techniques have been proposed with regard to reduced complexity and give better efficiency out of which the second approach of considering a feature vector of size 3 has been proved to be the best. It results in least computational complexity in query optimization and further gives 84.61% accuracy in detection of signs. This paper presents the comparison among various transforms for feature extraction from hand sign images. The proposed techniques for feature extraction are executed on a dataset of 260 images (consisting of 10 images of each alphabet).

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“…Scholars also proposed the method based on local characteristics, where face image was divided into several local blocks, handle each block independently, finally to make decisions based on all the pieces of mixed classification results [5]. For example, literature [6] proposed face sub-space reconstruction algorithm based on robust principal component analysis; literature [7] proposed joint sparse representation algorithm based on low-rank sub-space restore; literature [8] proposed sparse representation of reconstruction algorithm; and literature [9] proposed synergy representation based on the classification.…”

Section: Introductionmentioning

confidence: 99%

The Afresh Transform Algorithm Based on Limited Histogram Equalization of Low Frequency DCT Coefficients

Jun-peng¹,

Fang²,

Ji-nan³

2015

TOCSJ

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Abstract:In reality, face recognition due to illumination, expression, posture, or other objects caused by facial shade seriously affects the recognition rate of the problem, put forward the afresh transform algorithm based on limited histogram equalization of low frequency DCT coefficients. Firstly, divide the image into several non-overlapping local small fragments, and then use limited histogram equalization for local contrast stretching to realize and get rid of the local sub-block noise. Then, eliminate the illumination change in face image by reducing appropriate number of low frequency DCT coefficients. Finally, use kernel principal component analysis for feature extraction, and the nearest neighbor classifier to complete the final face recognition. In ORL, extension Yale B is used in one experiment to verify the effectiveness and the robustness of the proposed algorithm on outdoor facial database. The experimental results show that this algorithm, in dealing with robust face recognition, has achieved higher recognition rate as compared with several linear algorithm.

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Face Recognition Based on DCT and 2DLDA

Cited by 14 publications

References 9 publications

Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes

Novel Feature Extraction Technique for Indian Sign Language Recognition using Energy Compaction of Cosine Transform

The Afresh Transform Algorithm Based on Limited Histogram Equalization of Low Frequency DCT Coefficients

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