Neural network face recognition using statistical feature extraction

El‐Khamy, Said E.; Abdel-Alim, O.; Saii, Mariam

doi:10.1109/nrsc.2000.838960

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…El- Khamy et al (2000) proposed a new steganography technique for hiding images. It adopts both fractal and wavelet image processing techniques.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Image Generation Algorithm Using Fractals and Chaos Theory

Thamizhchelvy¹,

Geetha²

2015

Journal of Computer Science

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An Efficient Image Generation Algorithm is proposed. It generates Message Authentication Image (MAI) by using Fractals and chaos theory. The fractal images are generated by using Iterated Function System (IFS) techniques. We implemented and generated the fractal images exploring the properties of chaos. Chaos is an unpredictable behavior arises in a dynamical system so that the future behavior is not in a predictable way. Chaos is based on the initial condition that is generated by Pseudo Random Number Generator (PRNG). The chaotic behavior of the system is also analyzed. We use these Fractal images as a digital signature. This technique can be employed in online transactions like Banking, Shopping. to avoid phishing and also we can watermark this fractal image and use it for government and private identification proofs.

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“…El- Khamy et al (2000) proposed a new steganography technique for hiding images. It adopts both fractal and wavelet image processing techniques.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Image Generation Algorithm Using Fractals and Chaos Theory

Thamizhchelvy¹,

Geetha²

2015

Journal of Computer Science

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“…El-Khamy et al describe a method of human face recognition by the use of a neural network algorithm and statistical feature extraction [28]. The edge of the face image is detected by applying a Sobel filter in the preprocessing step.…”

Section: Face Detectionmentioning

confidence: 99%

Driver drowsiness monitoring based on yawning detection

Abtahi

Hariri

Shirmohammadi

2011

2011 IEEE International Instrumentation and Measurement Technology Conference

146

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This informed consent form is to make sure that you understand the nature of your involvement in this study, and to obtain your informed consent to participate in this study.Procedure: You will be asked to sit in the driver's seat of a parked car. Three different videos have to be taken from the frontal view of your face in conditions of yawning, talking, and normal closed mouth. The entire recording session will last about 5 minutes.Withdrawing from the study: Your participation in this study is voluntary. You may withdraw from the study at any time, by verbally informing the investigator or any of the researchers, even after signing the form. There will be no consequences following this action.Compensation: You will not receive monetary compensation for this study.Disclosure: You agree for the above-mentioned videos captured of you to be made available to researchers, from all over the world, for research and further study, on publically-accessible websites and for non-commercial purposes.Confidentiality: ther than the above "Disclosure", all other information about you collected during the study will be kept strictly confidential. Your name will not be associated with the collected data in any way. The data collection will be conducted by Dr. Shirmohammadi, his graduate students, or research fellows, or his research assistant.In closing: With your participation, you will be given a copy of this consent form. At the conclusion of the study, should you wish, you will be provided with a summary of the results. You may ask questions at any time, even after signing this consent form.Signatures: I have read the above description of the study and understand the conditions of participation. My signature indicates that I agree to participate in the study.

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“…계적 분류방법으로 주성분 분석(: PCA)에 의한 얼굴 인식 [2][3], [13], 서포트벡터머신(: SVM)에 의한 얼굴 인식 [2], [4], [11], 선형판별분석(: LDA) 의한 얼굴 인식 [3], [5], 독립성분분석(: ICA)에 의한 얼굴 인식 [6]과 신경망(: Neural Network)에 의한 얼굴 인식 [7], […”

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A Study on Face Recognition System Using LDA and SVM

Lee¹

2015

The Journal of the Korea institute of electronic communication

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This study proposed a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. The algorithm proposed detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). Also, by applying the feature vector obtained for SVM, face areas can be tested. After the testing, the feature vector is applied to LDA and using Euclidean distance in the 2nd dimension, the final analysis and matching is performed. The algorithm proposed in this study could increase the stability and accuracy of recognition rates and as a large amount of calculation was not necessary due to the use of two dimensions, real-time recognition was possible.

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Neural network face recognition using statistical feature extraction

Cited by 9 publications

References 15 publications

An Efficient Image Generation Algorithm Using Fractals and Chaos Theory

An Efficient Image Generation Algorithm Using Fractals and Chaos Theory

Driver drowsiness monitoring based on yawning detection

A Study on Face Recognition System Using LDA and SVM

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