Convolution based Face Recognition using DWT and feature vector compression

Sagar, G. Vidhya; Barker, Savita Y; Raja, K B; Babu, K. Suresh; Venugopal, K R

doi:10.1109/iciip.2015.7414814

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…The features obtained by set 1 and set 2 are concatenated to produce final features. Sagar et al, [2] introduced 2-D Discrete Wavelet Transform (DWT) to produce unique features. The pre-processed images are applied on DWT to produce LL features that are normalized to scale the magnitudes ranges from 0 to 1.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Harakannanavar¹,

Renukamurthy²,

Raja³

2018

IJIES

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Personal identification based on face recognition is receiving extensive attention over the last few years in both research and real-time applications due to increasing emphasis on security. In this paper, Face Recognition using the Fusion of bit-plane and binary image compression techniques is presented. Face images are resized to 256 ×256 to obtain the uniformity in the size of face images. The Bit-Plane compression algorithm is applied on the resized image to extract the features of face. Similarly, the Binary image compression technique is applied on 256× 256 resized images to extract the features. The features produced from both the compression techniques are added to obtain the final facial features. Finally, the test features and database features are compared using Euclidean Distance classifier. Extensive experiments are conducted on combined, Indian female, Indian male and JAFFE face database. The true success rate of the proposed model provides better recognition rate than other existing state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Harakannanavar¹,

Renukamurthy²,

Raja³

2018

IJIES

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“…Variety of methods exist for the conversion of spatial domain signal to spatial frequency domain. These methods include Fourier Transform [5], Wavelet Transform [6], and Wigner distribution [7]. However, these methods are not adaptive.…”

Section: Introductionmentioning

confidence: 99%

Spatial frequency based video stream analysis for object classification and recognition in clouds

Yaseen

Anjum

Antonopoulos

2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure.

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“…Feature (principal component) with the highest percentage (variance) selected to be used in the process of correspondence among feature of face images [29].…”

Section: ) Principal Component Analysismentioning

confidence: 99%

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

Santoso¹,

Harjoko²,

Putra³

2017

ijacsa

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Abstract-The main weakness of the k-Nearest Neighbor algorithm in face recognition is calculating the distance and sort all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved by utilizing the priority k-d tree search to speed up the process of k-NN classification. This paper proposes a method for student attendance systems in the classroom using facial recognition techniques by combining three levels of Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying the priority of k-d tree search to speed up the process of facial classification using kNearest Neighbor. The proposed algorithm is tested on two datasets that are Honda/UCSD video dataset and our dataset (AtmafaceDB dataset). This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor on our dataset is 95.56% with k = 5, whereas in the Honda / UCSD dataset it is only 82% with k = 3. The proposed method gives computational recognition time on our dataset 40 milliseconds.

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Convolution based Face Recognition using DWT and feature vector compression

Cited by 13 publications

References 5 publications

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

Spatial frequency based video stream analysis for object classification and recognition in clouds

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

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