An Automatic Feature Based Face Authentication System,

Arca, Stefano; Campadelli, Paola; Casiraghi, Elena; Lanzarotti, Raffaella

doi:10.1007/11731177_18

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…On the other hand, the analytical methods observe and measure local or distinctive human facial deformations such as eyes, eyebrows, nose, mouth etc. and their geometrical relationships in order to create descriptive and expressive models [2]. In the feature extraction process for expression analysis there are mainly two types of approaches which are the geometric feature based methods and the appearance based methods.…”

Section: Background Topicsmentioning

confidence: 99%

Recognizing Emotions from Facial Expressions Using Neural Network

Perikos

Ziakopoulos

Hatzilygeroudis

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Recognizing the emotional state of a human from his/her facial gestures is a very challenging task with wide ranging applications in everyday life. In this paper, we present an emotion detection system developed to automatically recognize basic emotional states from human facial expressions. The system initially analyzes the facial image, locates and measures distinctive human facial deformations such as eyes, eyebrows and mouth and extracts the proper features. Then, a multilayer neural network is used for the classification of the facial expression to the proper emotional states. The system was evaluated on images of human faces from the JAFFE database and the results gathered indicate quite satisfactory performance.

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Section: Background Topicsmentioning

confidence: 99%

Recognizing Emotions from Facial Expressions Using Neural Network

Perikos

Ziakopoulos

Hatzilygeroudis

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The proposed face recognition system consists of two (2) phases which are the enrollment and the recognition/verification phases as depicted in Figure 2. It consists of several modules which are Image Acquisition, Face Detection, Training, Recognition and Verification.…”

Section: System Overviewmentioning

confidence: 99%

“…PCA for face recognition is used in [2,3,4,6] is based on the information theory approach. It extracted the relevant information in a face image and encoded as efficiently as possible.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Enhancement of Neuro-eigenspace Face Recognition Using Photometric Normalization

Nazeer

Khalid

Omar

et al. 2007

Computer Graphics, Imaging and Visualisation (CGIV 2007)

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A face recognition system based on recent method which concerned with both representation and recognition using learning algorithm is presented. The learning algorithm, artificial neural network is used as a classifier for face recognition and face verification whereas the features are extracted using linear subspace techniques. This paper initially provides the overview of the proposed face recognition system, and explains the methodology used. It then explains the performance evaluation of the proposed system by applying two photometric normalization techniques: Histogram equalization and Homomorphic filtering, and comparing with Euclidean Distance and Normalized Correlation classifiers. The system produces promising results for face verification and face recognition where it achieved False Acceptance Rate (FAR) of 2.98% and False Rejection Rate (FRR) of 2.59% using ANN classifier with PCA feature extraction using homomorphic filtering, and 94.4% for recognition.

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“…Methodologies such as facial recognition using fiducial points, facial recognition using class specific linear projection, facial recognition using multilayer perceptron, facial recognition using local feature analysis and so on are generally used in facial recognition algorithms [6,15]. Facial recognition algorithms can be of different types [4,16].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Techniques for Authentication in Smart Devices - Comparative Study

George¹,

Tulasi²

2017

IJAIS

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With rapid development of technology there has been a surge in hand-held devices. These devices are looked upon as an alternative to the traditional devices like personal computer and laptop. As the storage and the processing capabilities of these devices are increasing they are been termed as smart devices. The amount of personal data stored in these devices has increased many folds. In order to ensure that the critical data that is stored in these devices is secured, it is essential to put in place authentication processes. Authentication can be done at multiple levels, ranging from a password to face recognition. Algorithms like Eigenfaces, Fisherfaces are being used as a part of authentication applications. This paper tries to provide a comparative study on most commonly used face recognition algorithms.

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An Automatic Feature Based Face Authentication System,

Cited by 5 publications

References 13 publications

Recognizing Emotions from Facial Expressions Using Neural Network

Recognizing Emotions from Facial Expressions Using Neural Network

Enhancement of Neuro-eigenspace Face Recognition Using Photometric Normalization

Face Recognition Techniques for Authentication in Smart Devices - Comparative Study

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