Facial Expression Recognition by Calculating Euclidian Distance for Eigen Faces Using PCA

Divya, M.; Prajwala, N.B.

doi:10.1109/iccsp.2018.8524332

Cited by 19 publications

(4 citation statements)

References 11 publications

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“…The holistic representation methods were applied on the whole image while the local representation methods applied on the local regions of image. Eigen faces [12] and Fisher faces [13] are two best examples for holistic representation and they have been widely used in face expression recognition [14]. However, these methods are not robust for pose variations and illumination changes in the facial images.…”

Section: Literature Surveymentioning

confidence: 99%

A Salient Binary Coding Scheme for Face and Expression Recognition from Facial Images

Seelothu¹,

Rao²

2021

IJIES

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The automatic Face Expression Recognition is very important in computer vision since it has widespread applications in real time. However, the main issue is the design of an efficient descriptor that describes the appearance changes on the face. Towards such design, in this paper we have introduced a novel binary compact coding called as Edge Adaptive Local Directional Binary Pattern (EALDBP) that encodes the texture features of facial image. The proposed EALDBP is resilient to noise and illumination variations as well as it encodes the directionality of expressions. The directionality is discovered by the computation of edge responses through different compass masks. Further to encode intensity variations, we have considered the relationship between gradient pixels in the local neighborhood. Finally, a compact face descriptor is constructed with the help of Histograms computation over the encoded face image. To evaluate the performance, we tested out approach on two datasets; they are CK+ and JAFFE. The average recognition accuracy of these two datasets is observed as 90.3898% and 93.5922% for JAFFE and CK+ respectively. These accuracies demonstrate that the proposed approach outperforms the several state-of-art methods.

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Section: Literature Surveymentioning

confidence: 99%

A Salient Binary Coding Scheme for Face and Expression Recognition from Facial Images

Seelothu¹,

Rao²

2021

IJIES

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“…Some studies tried to improve the facial recognition procedures, such as; Teng et al in [21] who proposed a 3D Convolutional Neural Networks (CNN) based architecture for FE recognition in videos. Mangala and Prajwala [22] used Eigenfaces and principle component analysis for the same purpose. Meanwhile, Ivanovsky et al [23] used CNN to detect smiles and FE.…”

Section: Facial Expression Recognition Applicationsmentioning

confidence: 99%

“…For instance, in [15,16] investigated the utilization of the selected features and landmarks for face recognition purposes only. Although the accuracy was the highest when both slopes and distances were used in [12], this study will use distances only as it analyzes which muscles and facial features are affected by FE, not for recognition purposes [21][22][23][24][25][26][27][28][29][30][31][32][33], evaluated the performance of FE classifications. While utilizing the periocular as a biometric trait in [33] has its failures when the face presents posture changes, occlusions, closed eyes, and other changes, in the FB, the recognition process can use other features than the one that exposes failure.…”

Section: The Effect Of Facial Expression On Face Biometric Reliabilitymentioning

confidence: 99%

The Effects of Facial Expressions on Face Biometric System’s Reliability

Alrubaish

Zagrouba

2020

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The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.

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“…Spatial PCA is applied to pixels and the temperature values to decide the relationships between the various temperature values measured for each pixel. Other recent research works acquired PCA for purposes like face recognition [25], dimensionality reduction of the feature space [26], and feature selection [27] which are different in nature of the application than frame selection by mapping into eigenspace.…”

Section: Related Researchmentioning

confidence: 99%