scholarly journals Reducing Dimensions of the Feature Vector of an Image Based on Blocking-DCT

2021 ◽  
Author(s):  
Farah Torkamani Azar
Keyword(s):  
Dimension Reduction ◽  
Feature Vector

Two approach for dimension reduction of a DCT block of an image to extracting features are provided.

scholarly journals Reducing Dimensions of the Feature Vector of an Image Based on Blocking-DCT

2021 ◽  
Author(s):  
Farah Torkamani Azar
Keyword(s):  
Dimension Reduction ◽  
Feature Vector

Two approach for dimension reduction of a DCT block of an image to extracting features are provided.

scholarly journals Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

2019 ◽  
Vol 8 (9) ◽  
pp. 2930-2937
Keyword(s):  
Dimension Reduction ◽  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Image Texture ◽  
Computational Time ◽  
Biometric Authentication ◽  
Person Identification ◽  
Face Features ◽  
Feature Dimension

The proposed work is a multimodal biometric authentication approach with image texture feature dimension reduction of trained feature vector which leads reduction in memory size and in turn reduces the computational time. In this paper hand and face features are used for person identification. The texture features of hand image are extracted using Haar and several Daubechie’s of 2D-DWT followed by 2D- edge detector gives better identification with reduction in feature vector and face features are extracted by neighborhood common characterization with block based segmentation approach to estimate the disparity in face. The neighborhood common characterization based structure recognition with a person representative per sample is more effective. The neighborhood features are constructed by extracting the similar blocks in the image, the intra pixel disparity feature is obtained by exploiting external common images to estimate the feasible facial disparities. Neighborhood common characterization reduces the overall residual of the given features over the local feature, common disparity dictionary, and shape based residual of a block. Neighbourhood common characterization representation, of face recognize with one representative per person more effectively. The system uses either of the biometric traits for person identification with 99.98% of authentication rate.

Deep Convolution Feature Vector for Fast Face Image Retrieval

2018 ◽  
Vol 30 (12) ◽  
pp. 2311
Author(s):  
Zhendong Li ◽  
Yong Zhong ◽  
Dongping Cao
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Face Image
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