Real time face recognition using DCT coefficients based face descriptor

Wijaya, I Gede Pasek Suta; Husodo, Ario Yudo; Arimbawa, I Wayan Agus

doi:10.1109/iac.2016.7905705

Cited by 7 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ITS face database belongs to Image Media Laboratory Kumamoto University, which is an ethnic East Asia face image, especially Japan and Chinese. ITS has 90 samples and each sample has (a) ORL dataset [12] (b) ITS dataset [4] (c) IND face dataset [2] Feature Extraction . Thirdly, India dataset is color face image dataset which has 61 persons (22 female and 39 male).…”

Section: Experiments and Results Discussionmentioning

confidence: 99%

“…The main aim of this work is to obtain strong face recognition against lighting variation which can be applied to the security system, i.e. door locking system which is an extended version of our previous work [2].…”

Section: Introductionmentioning

confidence: 99%

“…Face recognition has been widely developed by many researchers [3], such as statisticalbased (ICA, PCA, and naive Bayesian), global features-based, artificial intelligent-based (i.e., genetic algorithm, artificial neural network, SVM and etc,) and any their variations-based [1,2] face recognition algorithms. The most popular algorithm is face recognition based on subspace projection: LDA, eigenface (PCA), and their variations [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real Time Face Recognition Based on Face Descriptor and Its Application

2018

Self Cite

View full text Add to dashboard Cite

This paper presents a real time face recognition based on face descriptor and its application for door locking. The face descriptor is represented by both local and global information. The local information, which is the dominant frequency content of sub-face, is extracted by zoned discrete cosine transforms (DCT). While the global information, which also is the dominant frequency content and shape information of the whole face, is extracted by DCT and by Hu-moment. Therefore, face descriptor has rich information about a face image which tends to provide good performance for real time face recognition. To decrease the dimensional size of face descriptor, the predictive linear discriminant analysis (PDLDA) is employed and the face classification is done by kNN. The experimental results show that the proposed real time face recognition provides good performances which indicated by 98.30%, 21.99%, and 1.8% of accuracy, FPR, and FNR respectively. In addition, it also needs short computational time (1 second).

show abstract

Section: Experiments and Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real Time Face Recognition Based on Face Descriptor and Its Application

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…DCT was widely used in image coding in the past. The coefficients of DCT contain important features not only for coding but also for image recognition [19–23]. The DCT computations are given by (4)

F false(u, v false) = \frac{1}{\sqrt{2 N}} C (i) C (j) {false\sum}_{x = 0}^{N - 1} \sum_{y = 0}^{N - 1} f (x, y) \cos (][, \frac{false(2 x + 1 false) i π}{2 N}) \cos (][, \frac{false(2 y + 1 false) j π}{2 N})

The symbol ( u , v ) is the position of frequency domain; ( x , y ) is the position of spatial domain; F ( u , v ) is the coefficient at ( u , v ); f ( x , y ) is spatial pixels at ( x , y ); N is the block size.…”

Section: Database Establishment Of Face Imagesmentioning

confidence: 99%

“…Tan et al [18] proposed group sparsity and kernelised locality‐sensitive method, and the local similarity is measured instead of the Euclidian distance. To reduce the feature vectors, the frequency components of face are extracted by the transformation of discrete cosine transform (DCT) [19–23] and Fourier [24]. Atta and Ghanbari [19] proposed low computations using the analysis of second‐order DCT pyramid for face recognition.…”

Section: Introductionmentioning

confidence: 99%