Face recognition using spectrum-based feature extraction

Deepa, G.; Keerthi, R; Meghana, N.; Manikantan, K.

doi:10.1016/j.asoc.2012.04.015

Cited by 40 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Average RR(%) DFT + DCT [21] 80.23 Selective Illumination Enhancement + Thresholded DWT [22] 85.41 Histogram Equalization + Laplacian-Gradient Masking + DWT [23] 85.71 Background Removal + Scale Normalization + Log Transform [24] 86.14 Edge Tracked Scale Normalization + Log Transform [25] 88.00 DWT Dual-subband Frequency-domain Feature Extraction (DDFFE) + Threshold-Based Binary Particle Swarm Optimization (ThBPSO) [26] 86.81 Proposed Technique 91.21 (1,1,9,9) was chosen due to the high RR obtained. The Experimental Results for CMUPIE pose database for different training to testing ratios are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Face recognition using Gabor-Feature-based DFT shifting

Bhamidipati

rana

Manikantan

et al. 2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

Self Cite

View full text Add to dashboard Cite

The appearance of the face varies drastically when background and pose change. Variations in these conditions make Face Recognition (FR) an even more challenging and difficult task. In this paper we propose two novel techniques, viz., Gabor-Feature-based DFT Shifting (GFDS) and Skin-detectionbased Background Removal, to improve the performance of the FR system. GFDS is used to detect and neutralize the image variations like location, scale, pose etc., thereby enhancing Face Recognition. Skin-detection-based Background Removal is used to ascertain the shape of the face and distance from the camera to eliminate complex backgrounds. Experimental results show the promising performance of the proposed techniques for Face Recognition on three benchmark face databases, namely, Color FERET, Pointing Head Pose and CMUPIE databases.

show abstract

Section: Methodsmentioning

confidence: 99%

Face recognition using Gabor-Feature-based DFT shifting

Bhamidipati

rana

Manikantan

et al. 2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another feature extraction technique called Stationary Wavelet Transform (SWT) was used to overcome the pose related problems 5 . Frequency spectrum can also be used for feature extraction 6 . A method based on facial symmetry and DCT can also be used as a feature extractor 7 .…”

Section: Related Workmentioning

confidence: 99%

Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

Varadarajan¹,

Suhasini²,

Manikantan³

et al. 2015

Procedia Computer Science

Self Cite

View full text Add to dashboard Cite

Pose and illumination variation in Face Recognition (FR) is a problem of fundamental importance in computer vision. We propose to tackle this problem by using Chirp Z-Transform (CZT) and Goertzel algorithm as preprocessing, Block-based feature extraction and Exponential Binary Particle Swarm Optimization (EBPSO) for feature selection. Every stage of the FR system is examined and an attempt is made to improve each stage. The unique combination of CZT and Goertzel algorithm is used for illumination normalization. The proposed feature extractor uses a unique technique of Block based Additive Fusion of the image. EBPSO is a feature selection algorithm used to select the optimal feature subset. The proposed approach has been tested on four benchmark face databases, viz., Color FERET, HP, Extended Yale B and CMU PIE datasets, and demonstrates better performance compared to existing methods in the presence of pose and illumination variations.

show abstract

“…One level of decomposition downsamples each dimension of the image by a factor of two. In the proposed FR system, images from Color FERET, Pointing Head Pose and CMUPIE are subjected to two levels of decomposition [7].…”

Section: A Pre-processing Techniquesmentioning

confidence: 99%

“…The proposed relation to determine fitness by using trace of the matrices, M i and M o , is given by (7).…”

Section: Modified Binary Particle Swarm Optimization (Mbpso)mentioning

confidence: 99%

Face Recognition using Wavelet filtering and Dual Pose testing

Smrthi

Soumya

Manikantan

et al. 2013

2013 Annual IEEE India Conference (INDICON)

Self Cite

View full text Add to dashboard Cite

Face Recognition (FR) under varying pose, background and illumination conditions is challenging. In this paper, we propose four novel techniques, viz., Dual Pose testing (DPT), Color Based Background Removal (CBBR), Wavelet filtering and Modified Binary Particle Swarm Optimization (MBPSO), to improve the performance of a FR system. DPT is used to neutralize pose variations, CBBR removes cluttered background, Wavelet filtering is used for denoising and MBPSO, along with a modified fitness function, is used to reduce the number of selected features for recognition. Individual stages of the FR system are examined and an attempt is made to improve each stage. Experimental results show the promising performance of the proposed techniques for face recognition on four benchmark face databases, namely, Color FERET, Pointing Head Pose, CMUPIE and Extended YaleB.

show abstract

Face recognition using spectrum-based feature extraction

Cited by 40 publications

References 12 publications

Face recognition using Gabor-Feature-based DFT shifting

Face recognition using Gabor-Feature-based DFT shifting

Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

Face Recognition using Wavelet filtering and Dual Pose testing

Contact Info

Product

Resources

About