Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face

Satone, Manisha; Kharate, G. K.

doi:10.3745/jips.2012.8.3.483

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…Figure 11 shows few of the false matching results as well. Whereas, Table 1 shows the accuracy of our proposed model, this model is much better in terms of recognition as compared to the earlier systems (Sharma & Vashisht, 2012;Satone & Kharate, 2012). …”

Section: Resultsmentioning

confidence: 84%

Face Recognition using Average Half Face Template

Shehzad¹,

Awais²,

Amin³

et al. 2014

IJTech

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Face recognition is one of the most important technologies, which has been well-developed over the last two decades. Face recognition technology has reached a level of utmost importance as the security issues increase worldwide. Most of the previously proposed systems, based on half face images are computationally slow and require more storage. In the proposed model, an average half face image is used for recognition to reduce computational time and storage requirements. The Viola Jones method is used in conjunction with intensity-based registration for real time face detection and registration, before splitting the full face. Finally, Principal Component Analysis (PCA) is used to compress the multi-dimensional data space and recognition. Experimental results clearly elaborate that half face recognition produces much better results as compared to the full face recognition and other previously proposed half face recognition models.

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Section: Resultsmentioning

confidence: 84%

Face Recognition using Average Half Face Template

Shehzad¹,

Awais²,

Amin³

et al. 2014

IJTech

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“…The wavelet transforms (WT) are important tools which are widely used in feature extraction in many image processing measures like image compression and denoising applications [6] [7]. Wavelets are mathematical equations which produce varying frequency components of data.…”

Section: Pca With Wavelet Transformmentioning

confidence: 99%

Adaptive Principle Component Analysis to Improve Scale Invariant Feature Transform Matching for Face Recognition Applications

Jabbar¹

2016

IJAIASD

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Image matching using feature extraction is an important issue in computer vision tasks. The main drawback of matching process is the bottleneck problem that rapidly appeared when the number of features increased. This paper produced an adaptive approach to improve Scale Invariant Feature Transform (SIFT) matching. The main idea is to increase the number of SIFT points by using Adaptive PCA in wavelet domain. In this paper, the eigenface of PCA will entered to SIFT algorithm for feature matching, and thus only the SIFT features that belong to specific clusters are matched according to identified threshold. The performance of the adaptive method is implemented on AT&T ORL database. The APCA extends SIFT features matching in face images with its corresponding eigenfaces when compared with the result of the original PCA eigenfaces that entered to SIFT. As a result, the using of PCA in wavelet domain minimizes the size of the face image that entered to SIFT, this leads to increase the number of keypoints in face image and allowed to get best matching result, In addition to the ease of the adaptive method implementation.

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“…The Viola-Jones object detection framework proposed in 2001 by Paul Viola and Michael Jones [8,9] provides competitive object detection rates in realtime and was motivated primarily by the problem of face detection. In the future, this can also enable more advanced features like face image retrieval [10] or face recognition [11]. We use an algorithm that further improves Viola-Jones, based on work by Huang et al [12] and Abramson et al [13], in a JavaScript implementation made available by Liu [14].…”

Section: Face Detectionmentioning

confidence: 99%

Clustering Media Items Stemming from Multiple Social Networks

Steiner

Verborgh

Gabarró

et al. 2013

The Computer Journal

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We have created and evaluated an algorithm capable of deduplicating and clustering exact-and near-duplicate media items that get published and shared on multiple social networks in the context of events. This algorithm works in an entirely ad-hoc manner, without any pre-calculation. When people attend events, they more and more share event-related media items publicly on social networks to let their social network contacts relive and witness the attended events. In the past, we have worked on methods to accumulate such public user-generated multimedia content in order to summarize events visually, for example, in the form of media galleries or slideshows. In this paper, first, we introduce social-network-specific reasons and challenges that cause near-duplicate media items. Second, we detail an algorithm for the task of deduplicating and clustering exactand near-duplicate media items stemming from multiple social networks. Finally, we evaluate the algorithm's strengths and weaknesses and show ways to address the weaknesses efficiently.

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Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face

Cited by 16 publications

References 27 publications

Face Recognition using Average Half Face Template

Face Recognition using Average Half Face Template

Adaptive Principle Component Analysis to Improve Scale Invariant Feature Transform Matching for Face Recognition Applications

Clustering Media Items Stemming from Multiple Social Networks

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