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  Designing Algorithm for Excellent Classification Rate for Face Recognition Applications Using Exponential Local Discriminant Embedding  
  Authors : Poonam Tulsiram Bawankar; Hemlata Dakhore
  Cite as:


In this paper, we introduce a novel discriminant technique called "exponential LDE" (ELDE) for dimensionality reduction in Face recognition. The proposed ELDE can be seen as an extension of LDE framework in two directions [1]. First, the proposed framework overcomes the SSS problem without discarding the discriminant information that was contained in the null space of the locality preserving scatter matrices associated with LDE. Second, the proposed ELDE is equivalent to transforming original data into a new space by distance diffusion mapping (similar to kernel-based nonlinear mapping), and then, LDE is applied in such a new space. As a result of diffusion mapping, the margin between samples belonging to different classes is enlarged, which is helpful in improving classification accuracy. Identifying faces with facial expressions is also a challenging task, due to the deformation caused by the facial expressions [3]. To solve these issues, a preprocessing step was carried out after which Blur and Illumination-Robust Face recognition algorithm was performed. The test image and training images with facial expression are transformed to neutral face using Facial expression removal (FER) operation. Every training image is transformed based on the optimal Transformation Spread Function (TSF), and illumination coefficients. Local BinaryPattern (LBP) features extracted from test image and transformed training image is used for classification.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 311-321

Figures :05

Tables : 01


Poonam Tulsiram Bawankar : M.E. (W.C.C.) IVth sem, CSE Deptt., G. H. Raisoni Institute of Engineering and Technology, RTMNU, Nagpur, Maharashtra, India-440028.

Hemlata Dakhore : Assistant Professor, CSE department, G. H. Raisoni Institute of Engineering & Technology, RTMNU, Nagpur, Maharashtra, India-440028.


Discriminant analysis, face recognition, featureextraction, graph-based embedding, local discriminant embedding (LDE), small-sample-size (SSS) problem

A robust face recognition system for unconstrained environment was developed using ELDE algorithm. In this algorithm, LBPfeatures were extracted for the blurred, illuminated, expressionvariated probe image. Every image in the gallery set wastransformed using optimal TSF and their LBP features wereextracted. A simple pre-processing step, FER was carried outand the reconstructed face images have been used for furtherprocessing. LBP features of transformed image and blurredprobe image were compared to find the best match.It was observed that for ELDE algorithm, when Cropped Yale was used, the recognition rate obtained was 81.986% and for Yale face dataset, 87.88%. It was observed that for ELDE used, the recognition rate noticed was 82% and for Yale facedataset, 67.996%. The system works effortlessly and is robustto conditions like blur, illumination and expressions. The results were improved when expression was removed.


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