A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

ELLaban, Hend Ab.; Ewees, Ahmed A.; AbdElrazek, Elsaeed E.

doi:10.5120/ijca2017913009

Cited by 13 publications

(2 citation statements)

References 31 publications

(21 reference statements)

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“…For facial expression recognition, a real-time system is presented [8]. Student's 8 basic facial expressions can be recognized using this proposed system and expressions includes natural, surprise, sad, nervous, happy, feat, disgust and anger inside E-learning environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

Dar¹,

Palanivel²

2021

Adv. sci. technol. eng. syst. j.

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Face Recognition (FR) is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. Existing Machine Learning (ML) methods are failed to achieve optimal performance due to their inability to accurately extract the features from the face image, and enhancing system's recognition accuracy system becomes very difficult task. Majority of designed FR systems has two major steps: extraction of feature and classifier. Accurate FR system is still a challenge, primarily due to the higher computational time and separate feature extraction. In general, for various applications using images, deep learning algorithms are mostly recommended for solving these problems because it performs combined feature extraction and classification task. Deep learning algorithm reduces the computation time and enhances the recognition accuracy because of automatic extraction of feature. The major novelty of the work is to design a new VGG-16 with Transfer Learning algorithm for face recognition by varying active layers with three levels (3, 4, and 7). It also designs the Convolutional Neural Network (CNN) for FR system. The proposed work introduced a new Real Time Face Recognition (RTFR) system. The process is broken into three major steps: (1) database collection, (2) FR to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92. Second step, efficient RTFR algorithm is then used to recognize faces with a known database. Here two different deep learning algorithms such as CNN and VGG-16 with Transfer Learning are introduced for RTFR system. This proposed system is implemented using Keras. Thirdly the performance of these two classifiers is measured using of precision, recall, F1-score, accuracy and k-fold cross validation. From results it concludes that proposed algorithm produces higher accuracy results of 99.37%, whereas the other existing classifiers such as VGG3, VGG7, and CNN gives the accuracy results of 75.71%, 96.53%, and 69.09% values respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

Dar¹,

Palanivel²

2021

Adv. sci. technol. eng. syst. j.

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“…Informally speaking, they are the patterns most informative of the classification task. The kernel function generates the inner products to construct machines with different types of non-linear decision surfaces in the input space [10].…”

Section: Support Vector Machinementioning

confidence: 99%

Speech/music classification using PLP and SVM

R¹

2019

int. jour. eng. com. sci

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Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

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Review on Facial Expression Recognition System Using Machine Learning Techniques

Fathima

Vaidehi

2019

Learning and Analytics in Intelligent Systems

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A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

Cited by 13 publications

References 31 publications

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

Speech/music classification using PLP and SVM

Review on Facial Expression Recognition System Using Machine Learning Techniques

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