An efficient post-processing approach for dimensionality reduction methods for face recognition

Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to preprocessing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

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“…The data of the training template and testing template is then reconstructed. This paper is an extension of our previous work [30].…”

Section: Introductionmentioning

confidence: 70%

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

et al. 2018

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“…Irrelevant and redundant features can be eliminated with the help of feature reduction algorithms [19] or feature optimization techniques [20]. Faris et al [21] has discussed the salp swarm optimization algorithm for feature selection.…”

Section: Introductionmentioning

confidence: 99%

A novel sketch based face recognition in unconstrained video for criminal investigation

Lakshmi¹,

Arakeri

2023

IJECE

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Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.

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An efficient post-processing approach for dimensionality reduction methods for face recognition

Cited by 2 publications

References 15 publications

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

A novel sketch based face recognition in unconstrained video for criminal investigation

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