Heterogeneous face matching using geometric edge-texture feature ( GETF ) and multiple fuzzy-classifier system

Roy, Hiranmoy; Bhattacharjee, Debotosh

doi:10.1016/j.asoc.2015.12.006

Cited by 16 publications

(6 citation statements)

References 39 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The exaggeration of sketches are much more than CUFSF database and hence more challenging. The proposed method is tested with the existing state‐of‐the‐art methods (MCWLD [41], LG‐face [43], GEFT [45], LGFP [11], QPLMQ [26], and CDL [27]). Results of the stated methods are obtained from the published papers.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

Bhattacharya

Dasgupta²,

Routray³

2020

IET Image Processing

View full text Add to dashboard Cite

Section: Experiments and Resultsmentioning

confidence: 99%

“…This gives a total of 21 rank‐1 recognition rates for 21 tests. We evaluated our method against other methods: HE [65], LBP [5], LTV [39], LFA [40], LDN [42], LDP [38], LG‐face [43], LGFP [11] and GETF [45]. Comparison on rank‐1 recognition is shown in Table 4.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

Bhattacharya

Dasgupta²,

Routray³

2020

IET Image Processing

View full text Add to dashboard Cite

“…Earlier approaches proposed by the researchers are mainly application-oriented. Some of the popular approaches use the Eigenfaces [4], Fisherfaces [5], Laplacian faces [6], convolutional neural networks [7], [8] and others [9], [10] for recognition purpose. These algorithms work fine under a controlled environment, but most of them fail to achieve their reputations when the situation involves variation in illumination, pose, facial expression, aging, partial occlusion, etc.…”

Section: Introductionmentioning

confidence: 99%

LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition

Kar

Pramanik

Chakraborty

et al. 2021

IEEE Trans.Inform.Forensic Secur.

Self Cite

View full text Add to dashboard Cite

In this work, we proposed a novel method, called Local Modified Zernike Moment per unit Mass (LMZMPM), for face recognition, which is invariant to illumination, scaling, noise, in-plane rotation, and translation, along with other orthogonal and inherent properties of the Zernike Moments (ZMs). The proposed LMZMPM is computed for each pixel in a neighborhood of size 3 × 3, and then considers the complex tuple that contains both the phase and magnitude coefficients of LMZMPM as the extracted features. As it contains both the phase and the magnitude components of the complex feature, it has more information about the image and thus preserves both the edge and structural information. We also propose a hybrid similarity measure, combining the Jaccard Similarity with the L1 distance, and applied to the extracted feature set for classification. The feasibility of the proposed LMZMPM technique on varying illumination has been evaluated on the CMU-PIE and the extended Yale B databases with an average Rank-1 Recognition (R1R) accuracy of 99.8% and 98.66% respectively. To assess the reliability of the method with variations in noise, rotation, scaling, and translation, we evaluate it on the AR database and obtain an average R1R higher than that of recent state-of-the-art methods. The proposed method shows a very high recognition rate on Heterogeneous Face Recognition as well, with 100% on CUFS, and 98.80% on CASIA-HFB.

show abstract

“…Some of the algorithms [12][13][14][15][16] are only for sketch vs. photo matching and some algorithms [17,18] are exclusively for NIR vs. VIS matching. Few algorithms [1,19,20,21,22] have been developed for both sketch-photo and NIR-VIS matching. It means that for different types of problems different algorithms have been established.…”

Section: Introductionmentioning

confidence: 99%

Local-Friis-Radiation-Pattern (LFRP) for Face Recognition

Roy¹,

Koley²

2021

Sens Imaging

Self Cite

View full text Add to dashboard Cite

In forensic science, various circumstances and requirements give rise to the need for the development of the inter-modality face recognition system to reinforce law and order. In this paper, a novel methodology called Local-Friis-Radiation-Pattern (LFRP) for both homogeneous (visible face images with variations like illumination, pose, and makeup) and heterogeneous (near-infrared (NIR)-visible (VIS) and sketch-photo images) face recognition is proposed. LFRP incorporates the renowned Friis equation of antenna radiation and extends it to image pixels to establish a relation among the pixels residing in a local neighbourhood. Here, we present a robust local image descriptor called the LFPR to effectively capture the illumination-invariant, and modality-invariant facial features. Recognition results on CASIA NIR-VIS 2.0, CUFSF, LFW, CMU-PIE, Extended Yale B, TUFTS VMU, YMU, and MIW databases indicate the superiority and efficiency of the proposed scheme in terms of common feature representation under varying illumination, pose and modality. Moreover, experimental findings reveal that the proposed LFRP is robust against face recognition under non-permanent facial cosmetics (makeup). On the more, a predefined convolutional neural network architecture has been incorporated to improve and compare the proposed LFRP feature map with other state-of-the-art deep learning based methods.

show abstract

Heterogeneous face matching using geometric edge-texture feature ( GETF ) and multiple fuzzy-classifier system

Cited by 16 publications

References 39 publications

Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

Multi‐directional local adjacency descriptors (MDLAD) for heterogeneous face recognition

LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition

Local-Friis-Radiation-Pattern (LFRP) for Face Recognition

Contact Info

Product

Resources

About