High- performance facial expression recognition using Gabor filter and Probabilistic Neural Network

Fazli, Saeid; Afrouzian, Reza; Seyedarabi, Hadi

doi:10.1109/icicisys.2009.5357716

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…There are various methods for feature reduction like LDA, PCA, and ICA. Saeid Fazli et al [6] founded in their study that if the number of samples is less as compared to the dimensionality of the image then LDA alone is insufficient for feature reduction. To increase the performance PCA should be used before LDA.…”

Section: Literature Reviewmentioning

confidence: 99%

Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA

Shah¹,

Khanna²

2015

IJCA

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Facial expression recognition is an interesting and challenging problem, and found in many applications like humancomputer interaction (HCI), robotics, video surveillance, border security, clinical research, person verification, crime prevention etc.. Facial expression is the movement of the muscles beneath the skin of the face. Through facial expressions human can convey their emotions without any verbal means. In this paper we have created raw database of color images. Training and testing set of images are created. Color information in an image is used to detect the face from the image. Important features from the detected face are extracted to form feature vectors using Gabor and Log Gabor filters. Principal Component Analysis (PCA) is used to reduce the dimension of the extracted features. Then these reduced features are classified using Euclidean distance. The main aim is to work upon three emotions-happy, neutral, surprise. Experiment carried out on self-generated database show comparable performance between Gabor and Log Gabor filters, where Log Gabor filters outperforming Gabor filters with classification accuracy of 86.7%.

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Section: Literature Reviewmentioning

confidence: 99%

Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA

Shah¹,

Khanna²

2015

IJCA

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“…Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. These cues may also complement speech by helping the listener to elicit the intended meaning of spoken words [10].…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

“…The Viola-Jones object detection framework [10] is the first object detection framework to provide competitive object detection rates in real-time proposed in 2001 by Paul Viola and Michael Jones. Even though it can be trained to detect a variety of object classes, it was motivated mainly by the problem of face detection.…”

Section: ) Viola Jones Face Detection Algorithmmentioning

confidence: 99%

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Survey on an Accurate Facial Emotion Recognition System Based on Pca

2017

IJAERD

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“…Turning of head can be eliminated by choosing central points of the eyes manually. Then the images are turned up to a point that the X parameter gets the same dimensions as the central points of the eye [12]. Then the face is cropped in rectangular according to face model explained in [8].…”

Section: Image Pre-processingmentioning

confidence: 99%

A Survey of Facial Expression Recognition Methods

Shah¹

2014

IOSRJEN

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-Facial expression recognition (anger, sad, happy, disgust, surprise, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression is one of the most powerful and natural means for human beings to show their emotions. It has found its applications in humancomputer interaction (HCI), robotics, border security systems, forensics, machine vision, video conferencing, user profiling for customer satisfaction, physiological research etc. Although humans can detect facial expressions with less effort and delay but it is still a challenge for the machine to fast and effectively detect facial expressions. Therefore algorithms should be developed to thought machines to understand facial gestures. This paper focuses on a review of different techniques for facial expression recognition.

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High- performance facial expression recognition using Gabor filter and Probabilistic Neural Network

Cited by 17 publications

References 14 publications

Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA

Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA

Survey on an Accurate Facial Emotion Recognition System Based on Pca

A Survey of Facial Expression Recognition Methods

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