A Face Recognition Framework for Illumination Compensation Based on Bio-Inspired Algorithms

Plichoski, Guilherme Felippe; Chidambaram, Chidambaram; Parpinelli, Rafael Stubs

doi:10.1109/bracis.2018.00056

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The FR framework provides techniques for preprocessing and feature extraction in which the optimization process, carried out by the bio-inspired algorithm, gives as output the best set of techniques for the classification task [10]. Besides that, the parameters of selected techniques are also optimized by the framework as well as the distance metric employed by the classifier.…”

Section: The Fr Frameworkmentioning

confidence: 99%

“…In [10], an FR framework is presented and features the ability to self-adapt to conditions inherent in the database's images. The intent of the framework is to provide techniques for preprocessing and for feature extraction in which the optimization process, carried out by the optimization algorithm, gives as output the best set of techniques and parameters for the task.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is well known that fixed parameters are not robust to all optimization problems and to tune these parameters is not trivial [11]. Hence, this paper is an extension of the work presented in BRACIS 2018 [10] and differs from previous work in two points: i) we employ the FR framework using a self-adaptive version of DE [12], called FRjDE, in order to control the parameters of mutation factor (F ) and crossover probability (CR). The goal is to minimize the number of parameters to adjust and analyze the performance of the framework; and ii) we used two well-known databases i.e.…”

Section: Introductionmentioning

confidence: 99%

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A face recognition framework using self-adaptive differential evolution

Plichoski¹,

Chidambaram²,

Parpinelli³

2019

Learn. Nonlin. Mod.

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It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. Also, to collect the necessary amount of images is hard to guarantee in many situations, e.g. ID cards, drivers licenses or visas, leading to the one sample per person (OSPP) problem. This work addresses the OSPP problem along with illumination and pose variation using an FR framework composed of a self-adaptive Differential Evolution, named FRjDE. The main feature of the framework stands on the use of the optimization algorithm for choosing which preprocessing and feature extraction strategies to use, as well as tunning their parameters. Also, by using the jDE algorithm, F and CR control parameters are self-adapted. Experiments are made using two well-known databases, named CMU-PIE and FERET. Results obtained from the FRjDE approach are compared against the FR framework using the standard DE algorithm and against results found in the literature. Results suggest that the proposed approach is highly competitive and well suited for face recognition.

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Section: The Fr Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A face recognition framework using self-adaptive differential evolution

Plichoski¹,

Chidambaram²,

Parpinelli³

2019

Learn. Nonlin. Mod.

Self Cite

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A face recognition framework based on a pool of techniques and differential evolution

Plichoski

Chidambaram

Parpinelli

2021

Information Sciences

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A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition

Zhou

Zhong³

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Computational efficiency is critical to many mobile and always-on face recognition applications. To this end, a heterogeneous spiking neural network (SNN) is proposed for face recognition. To obtain high recognition accuracy at minimal computational overheads, the heterogeneous SNN consists of an encoding subnet for sparse image feature encoding and classification subnet for feature classification. The experimental results suggest that the proposed heterogeneous algorithm can achieve high recognition accuracy on small datasets of human face samples with labeled identities at a high computational efficiency with very low neuronal activities. The proposed SNN is promising for low-cost mobile or always-on systems with strictly constrained resource and energy budgets.

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A Face Recognition Framework for Illumination Compensation Based on Bio-Inspired Algorithms

Cited by 4 publications

References 8 publications

A face recognition framework using self-adaptive differential evolution

A face recognition framework using self-adaptive differential evolution

A face recognition framework based on a pool of techniques and differential evolution

A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition

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