[PDF] Top 20 Research on Face Detection based on Deep Learning

... traditional face detection algorithm, in this paper, the author used the convolution neural network model of the deep learning method, and designed the face detector based on the ... See full document

... years, deep learning has solved many unsolved problems in the field of artificial intelligence with its excellent fea- ture learning ability and has achieved rapid develop- ...ment. Research ... See full document

... the face recognition one of the most significant biometrics authentication ...video face recognition is the key issue from security viewpoint. Video Face recognition has been considered for over two ... See full document

... image based center-surround method for video stream by adding another temporal ...model based on the similarity implied redundancies amongst image ...saliency detection in ...saliency ... See full document

... improve face recognition accuracy and performance by using separate networks to capture information from several regions and scaling to a structure of powerful face ...and detection scores for a ... See full document

... for deep learning models, medical data are ...medical research, but there is no standardized method to evaluate the EHRs ’ quality, which means the accuracy of disease diagnosis using electronic ... See full document

... The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of ... See full document

... Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted ...novel deep learning neural network architectures for automatically estimating if the child is ... See full document

... of face detection methods through the image resizing strategy presented in [35] for a wide variety of hardware ...representative face detection methods, namely MTCNN, PyramidBox and DSFD, ... See full document

... There has been researching done about the use of machine intelligence to analyze power system faults. Power system fault features have been used to train several neural network architectures such as probabilistic neural ... See full document

... Automatic detection of animal vocalizations, such as singing birds, has a scientific challenge in itself, can be helpful for monitoring biodiversity. Various researches are involved in bioacoustics, the recent ... See full document

... integrating deep learning technology into industrial production, improving the efficiency of traditional industrial produc- tion, and reducing the repetitive labor force, and liberating people from the re- ... See full document

... Typically for detecting MURA defects for OLED/LED/LCD panels, images are captured in different wavelengths of light and then passed on to a learning system for automatic detection and classification. This ... See full document

... for deep learning in the field of machine learning. Deep learning, as a kind of emerging learning algorithm of multi-layer neural network, has solved the local minimum defect in ... See full document

... CNN based face recognition architecture, named as Deep hidden IDentity (DeepID), consists of network fusion of ...[8] face recognition approach, During preprocessing stage the face ... See full document

... from face images is becoming increasingly significant for law enforcement and intelligence ...given face images. The project uses Deep Learning Technology where Convolutional Neural Network ... See full document

... blended learning-based on athletic subject products are ...last research was conducted by Gusdiyanto [16] with the of producing a learning product based on blended learning ... See full document

... The proposed system utilizes Convolutional Neural Network. CNN is used to recognize the essential and unimportant features from the image dataset. The proposed work concentrates on training CNN model so that it will ... See full document

... Recently, there have been two approaches observed in the deep learning based object detectors: i) One stage approach, ii) Two stage approach. Two stage approach involves first generating region ... See full document

... Figure 5. (a) The learning curves of the proposed method with contrastive loss and supervised learning respectively. (b) The energy term of the proposed pairwise learning method of CFFN ... See full document