Periocular recognition: how much facial expressions affect performance?

Barroso, Elisa M.; Santos, Gil; Cardoso, Luís Fernando Cardoso e; Padole, Chandrashekhar; Proença, Hugo

doi:10.1007/s10044-015-0493-z

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…We employ five different descriptors for feature extraction, and prediction is made with SVM classifiers. Despite some studies have analyzed the impact of expression changes on the performance of periocular recognition systems [29], [30], this is, to the best of our knowledge, the first study using periocular images for the task of predicting expressions. In this initial study, we carry out prediction on individual frames.…”

Section: Discussionmentioning

confidence: 99%

Expression Recognition Using the Periocular Region: A Feasibility Study

Alonso‐Fernandez

Bigün

Englund

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.

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Section: Discussionmentioning

confidence: 99%

Expression Recognition Using the Periocular Region: A Feasibility Study

Alonso‐Fernandez

Bigün

Englund

2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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“…It is possible that in the future, automatic facial expression recognition may play an important role in human-computer interaction. Indeed, many researchers are working on this topic and finding effective solutions for facial features extraction and classification (Barroso, Santos, Cardoso, Padole, & Proença, 2016;Quan, Matuszewski, & Shark, 2016;Yifrach, Novoselsky, Solewicz, & Yitzhaky, 2016). However, one prevailing issue is that most current research uses labbased facial expression images or image sequences.…”

Section: Discussionmentioning

confidence: 99%

A survey on computer vision techniques for detecting facial features towards the early diagnosis of mild cognitive impairment in the elderly

Fei

Yang

et al. 2019

Systems Science & Control Engineering

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“…Table 1 tabulates the architecture of the proposed network. RGB (9) RGB (11) RGB (13) RGB (15) OCLBCP (2) OCLBCP (4) OCLBCP (6) OCLBCP (8) OCLBCP (10) OCLBCP (12) OCLBCP (14) OCLBCP (16) maxpool maxpool maxpool maxpool RGB maxpool maxpool maxpool maxpool OCLBCP sum (1) (2)…”

Section: Rgb-oclbcp Of Dual-stream Cnnmentioning

confidence: 99%

“…For instance, the appearances of subjects such as cosmetic products, plastic surgery or wearing masks may cause the failure of identifying the suspects. To hinder the complexity of the facial region, periocular recognition is gaining attention these days attributed to its promising recognition performance [4].…”

Section: Introductionmentioning

confidence: 99%

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

2019

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Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. The proposed network aggregates the RGB image and the OCLBCP descriptor by using two distinct late-fusion layers. We demonstrate that the proposed network benefits from the RGB image and thee OCLBCP descriptor can gain better recognition performance. A new database, namely an Ethnic-ocular database of periocular in the wild, is introduced and shared for benchmarking. In addition, three publicly accessible databases, namely AR, CASIA-iris distance and UBIPr, have been used to evaluate the proposed network. When compared against several competing networks on these databases, the proposed network achieved better performances in both recognition and verification tasks.

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Periocular recognition: how much facial expressions affect performance?

Cited by 13 publications

References 23 publications

Expression Recognition Using the Periocular Region: A Feasibility Study

Expression Recognition Using the Periocular Region: A Feasibility Study

A survey on computer vision techniques for detecting facial features towards the early diagnosis of mild cognitive impairment in the elderly

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

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