Ring Segmented and Block Analysis Based Multi-feature Evaluation Model for Contrast Balancing

Juneja, Kapil

doi:10.1007/978-981-10-6544-6_18

Cited by 3 publications

(2 citation statements)

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“…The ring [27] segmentation method was applied to take dynamic and feature adaptive decision to handle the contrast issue in facial recognition. Deng et al [30] has provided a work on lighting aware face recovery and descriptor using fiducial landmarks.…”

Section: Related Workmentioning

confidence: 99%

“…Face expression [24] classification, individual identification of group [29] [25], gender identification, age-estimation are the most common application worked by the researchers in recent years. These applications are having the challenges of occlusion [25][26], illumination [19][21] [26] variance, contrast [27] unbalancing, head or pose [28] precise variations, etc. The researchers have provided numerous algorithms to rectify the images against these challenges or to extract the variant robust features so that the accuracy of face recognition can be improved in existence of these challenges.…”

Section: Introductionmentioning

confidence: 99%

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Multi Featured Fuzzy based Block Weight Assignment and Block Frequency Map Model for Transformation Invariant Facial Recognition

Juneja¹,

Rana²

2018

IJIGSP

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Abstract-Misalignment of the camera, some jerk during capture is natural that results some tilt or geometric transformed photo. The accurate recognition on these misaligned facial images is one of the biggest challenges in real time systems. In this paper, a fuzzy enabled multiparameter based model is presented, which is applied to individual blocks to assign block weights. At first, the model has divided the image into square segments of fixed size. Each segmented division is analyzed under directional, structural and texture features. Fuzzy rule is applied on the obtained quantized values for each segment and to assign weights to each segment. While performing the recognition process, each weighted block is compared with all weighted-feature blocks of training set. A weight-ratio to exactly map and one-to-all map methods are assigned to identify overall matching accuracy. The work is applied on FERET and LFW datasets with rotational, translational and skewed transformation. The comparative observations are taken against KPCA and ICA methods. The proportionate transformation specific observations show that the model has improved the accuracy up to 30% for rotational and skewed transformation and in case of translation the improvement is up to 11%.

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Section: Related Workmentioning

confidence: 99%