Face detection based on multilayer feed‐forward neural network and Haar features

Owusu, Ebenezer; Abdulai, Jamal Deen; Zhan, Yongzhao

doi:10.1002/spe.2646

Cited by 15 publications

(8 citation statements)

References 29 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Multilayer Perceptron (MLP) network is trained using the backpropagation [ 30 ], which uses data to adjust the network's weights and thresholds to minimize the error in its predictions on the training set. First, it computes the total weighted input x j , using the following equation:

where y i is the activity level of the j -th unit in the previous layer and w ij is the weight of the connection between the i -th and the j -th unit.…”

Section: Methodsmentioning

confidence: 99%

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Owusu

Boakye-Sekyerehene

Appati

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

show abstract

where y i is the activity level of the j -th unit in the previous layer and w ij is the weight of the connection between the i -th and the j -th unit.…”

Section: Methodsmentioning

confidence: 99%

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Owusu

Boakye-Sekyerehene

Appati

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Face detection is performed on videos having low resolutions ranging from 400*300 to 200*150 for each frame. The frame rate is around 25 fps giving Haar-based face detection an average time of 100ms per frame [ 50 ]. Since super-resolution using the given frames takes an estimated 1s for 7-8 frames, the detection, super-resolution, and recognition rate are selected as 6 fps.…”

Section: Experimental Setup and Analysismentioning

confidence: 99%

Towards a super-resolution based approach for improved face recognition in low resolution environment

Singh

Rathore

Kumar

2022

Multimed Tools Appl

View full text Add to dashboard Cite

The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object in surveillance data is intriguing yet difficult due to the low resolution of captured images or video. The super-resolution approach aims to enhance the resolution of an image to generate a desirable high-resolution one. This paper develops a robust real-time face recognition approach that uses super-resolution to improve images and detect faces in the video. Many previously developed face detection systems are constrained by the severe distortion in the captured images. Further, many systems failed to handle the effect of motion, blur, and noise on the images registered on a camera. The presented approach improves descriptor count of the image based on the super-resolved faces and mitigates the effect of noise. Furthermore, it uses a parallel architecture to implement a super-resolution algorithm and overcomes the efficiency drawback increasing face recognition performance. Experimental analysis on the ORL, Caltech, and Chokepoint datasets has been carried out to evaluate the performance of the presented approach. The PSNR (Peak Signal-to-Noise-Ratio) and face recognition rate are used as the performance measures. The results showed significant improvement in the recognition rates for images where the face didn’t contain pose expressions and scale variations. Further, for the complicated cases involving scale, pose, and lighting variations, the presented approach resulted in an improvement of 5%-6% in each case.

show abstract

“…Ebenezer Owusu et al, 2018 [4] has shown the face detection method using the haar feature extraction and classification by MFNN. His motive for this study was improving its accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Face Detection Using Haar Cascades Classifier

Kaur

Mirza²

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

View full text Add to dashboard Cite

In the last several years, face detection has been listed as one of the most engaging field in research. Face detection algorithms is used for detection of frontal human faces. Face detection find use is many applications such as face tracking, faces analysis and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study we would be focusing on some of the face detection technology in use.

show abstract

Face detection based on multilayer feed‐forward neural network and Haar features

Cited by 15 publications

References 29 publications

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Towards a super-resolution based approach for improved face recognition in low resolution environment

Face Detection Using Haar Cascades Classifier

Contact Info

Product

Resources

About