Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost

Hiremath, P. S.; Hiremath, Manjunatha

doi:10.5815/ijigsp.2014.01.05

Cited by 5 publications

(5 citation statements)

References 26 publications

(19 reference statements)

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“…In another work, in [57], a face verification system implementation was carried out using Gaussian Face and it obtained a surpassed recognition rate on the LFW dataset. In [70] authors used a combination of Radon Transform to crop facial areas, Gabor filter, LDA for dimensionality reduction, and AdaBoost for face recognition. The system achieved a high accuracy rate on three 3D datasets.…”

Section: ) Other Non-ai Techniques For Face Detection and Recognitionmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

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Section: ) Other Non-ai Techniques For Face Detection and Recognitionmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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“…These region based approaches [5,6] have shown excellent retrieval performance however with high computational time and with too many dimensional features. The methods based on local features are extensively used in various computer vision and image processing applications and achived excellent results [7][8][9][10][11][12][13][14][15] The local features of image are color, shape, texture, edge and etc.… Among these local features the color is the most significant and prominent feature and have profound impact on human perception. The color based methods [16,17] are very popular in CBIR due to their effectiveness and low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval based Local Motif Patterns Code

Obulesu¹,

Kumar²,

Sumalatha³

2018

IJIGSP

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We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach [54]. Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.

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“…Among three points, 'Z' points are the depth data (as shown in figure 4) used to create range face image. Thus, disguise problem due to makeup is automatically discarded during 3D face image based recognition [16] process.…”

Section: Depth Analysismentioning

confidence: 99%

“…During the localization of occlusion, an ERFI model [16] from each subject is developed by the authors. This model actually preserves all the depth energies from the frontal posed range face image.…”

Section: Occlusion Localizationmentioning

confidence: 99%

Depth based Occlusion Detection and Localization from 3D Face Image

Ganguly¹,

Bhattacharjee²,

Nasipuri

2015

IJIGSP

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Abstract-In this paper, authors have proposed two novel techniques for occlusion detection and then localization of the occluded section from a given 3D face image if occlusion is present. For both of these methods, at first, the 2.5D or range face images are created from input 3D face images. Then for detecting the occluded faces, two approaches have been followed, namely: block based and threshold based. These two methods have been investigated individually on Bosphorus database for localization of occluded portion. Bosphorus database consists of different types of occlusions, which have been considered during our research work. If 2D and 3D images are compared then 3D images provide more reliable, accurate, valid information within digitized data. In case of 2D images each point, named as pixel, is represented by a single value. One byte for gray scale and three byte for color images in a 2D grid whereas in case of 3D, there is no concept of 2D grid. Each point is represented by three values, namely X, Y and Z. The 'Z' value in X-Y plane does not contain the reflected light energy like 2D images. The facial surface's depth data is included in Z's point set. The threshold or cutoff based technique can detect the occluded faces with the accuracy 91.79% and second approach i.e. block based approach can successfully detect the same with the success rate of 99.71%. The accuracy of the proposed occlusion detection scheme has been measured as a qualitative parameter based on subjective fidelity criteria.

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Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost

Cited by 5 publications

References 26 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

Image Retrieval based Local Motif Patterns Code

Depth based Occlusion Detection and Localization from 3D Face Image

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