Face recognition using Principle Component Analysis and Linear Discriminant Analysis

Mahmud, Firoz; Khatun, Mst Taskia; Zuhori, Syed Tauhid; Afroge, Shyla; Aktar, Mumu; Pal, Biprodip

doi:10.1109/iceeict.2015.7307518

Cited by 30 publications

(8 citation statements)

References 6 publications

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“…From the face, humans can be distinguished and recognized more quickly and easily [1]. Therefore the face is used as a means of identification of a person or face recognition [3] Generally, the image recognition system is divided into 2 types, namely: feature-based system and image-based system.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Using Fisherface Method

Anggo

Arapu

2018

J. Phys.: Conf. Ser.

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Abstract. Fisherface is one of the popular algorithms used in face recognition, and is widely believed to be superior to other techniques, such as eigenface because of the effort to maximize the separation between classes in the training process. The purpose of this research is to establish a program of face recognition application using fisherface method by utilizing GUI applications and databases that are used in the form of a Papuan facial image. Image recognition using fisherface method is based on the reduction of face space dimension using Principal Component Analysis (PCA) method, then apply Fisher's Linear Discriminant (FDL) method or also known as Linear Discriminant Analysis (LDA) method to obtain feature of image characteristic. The algorithm used in the process for image recognition is fisherfaces algorithm while for identification or matching face image using minimum euclidean. The method used in this study is literature study that is studying and reviewing various books or literature related to mathematical concepts that underlies the formation of fisherface algorithm to recognize the image of a person's face which is then applied in programming language, especially programming language Matlab7.10. While in the process of preprocessing used Adobe Photoshop CS4 application program, its goal is to make the face image to be uniform in terms of size and format so that the image is ready to be used by the system. The results show that for image recognition where the image of testing is the same as the training image, the percentage of program success is 100%, while for 73 facial test images with various expressions and various positions, 70 faces are recognized correctly and 3 faces are recognized incorrectly, so the percentage of success is 93%..

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Section: Introductionmentioning

confidence: 99%

Face Recognition Using Fisherface Method

Anggo

Arapu

2018

J. Phys.: Conf. Ser.

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“…Tong et al [18] introduced a new local gradient Code (LGC), which was a variant on the LBP results by describing the gradient of the horizontal, vertical, and diagonal detail information of the facial image. Independent Component Analysis (ICA) [19], Eigenfaces using Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) [20][21][22], Histogram of Orientation Gradient (HOG) [23] and Wavelets [24] are also widely used for FR and FER systems. The different forms of Eigenfaces are used as a base for other face recognition techniques.…”

Section: Related Workmentioning

confidence: 99%

Novel Descriptors for Effective Recognition of Face and Facial Expressions

Ayeche¹,

Alti²

2020

RIA

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In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

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“…There are several parts of the human body that are used for identification process, such as eyes, fingerprints, or faces. The face is the part that is easier to recognize [1]. For recognizing, the face is detected by computer technology that digitize a human face in an image.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Real-Time Face Detection

Fawzi,

Fat,

Wulandari

2023

ijaste

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Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The face detection process in this study was simulated using JupyterLab. The result showed that LBPH method obtained the highest level of accuracy in between comparison among Fisherface method and the Eigenface method. The accuracy of the LBPH method is 93.90%, while the Eigenface method is 85% and the Fisherface method is 53.33%. Differences in face detection accuracy were found due to the low level of lighting in the room and the use of accessories on the subject.

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Face recognition using Principle Component Analysis and Linear Discriminant Analysis

Cited by 30 publications

References 6 publications

Face Recognition Using Fisherface Method

Face Recognition Using Fisherface Method

Novel Descriptors for Effective Recognition of Face and Facial Expressions

Evaluation Method of Real-Time Face Detection

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