Face Recognition: A Fourier Transform and SVD Based Approach

Jha, Animesh Kumar; Gupta, Rupak; Saini, Dharmender

doi:10.1109/cicn.2013.51

Cited by 5 publications

(3 citation statements)

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“…Another approach to extract holistic information is feature-based approach such as Wavelet transform [5], Discrete cosine transform [6], and Fourier transform [7]. In this approach the features are extracted from spatial frequency technique.…”

Section: Extraction Of Local Featuresmentioning

confidence: 99%

“…To recognize unknown faces, the Euclidian distance method is used to find the minimum distance between data projection; face tests and the face train. Euclidean distance is defined as the straight-line distance between two points as given in (7). The smallest value of ϵ_k distance will make the assumption that they are from the same class.…”

Section: Classification Processmentioning

confidence: 99%

“…The smallest value of ϵ_k distance will make the assumption that they are from the same class. (7) where:…”

Section: Classification Processmentioning

confidence: 99%

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Face recognition using assemble of low frequency of DCT features

2019

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Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor.

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Section: Extraction Of Local Featuresmentioning

confidence: 99%

Section: Classification Processmentioning

confidence: 99%