Facial Expression analysis is an interesting and challenging problem and has applications in many areas such as human computer interaction and robotics. Deriving an effective facial representation from original face images is an important step for successful facial expression recognition. In this paper, we are evaluating 2DPCA and LBP+2DPCA for facial representation. The three stages of facial expression recognition are pre-processing, features extraction and classification. Many researchers' uses face detection as a pre-processing step which improves the accuracy but also increases the time complexity of the system. To reduce the computational complexity we propose to apply 2DPCA on input images directly. Our system has achieved high accuracy as well as very low time complexity. This system is suitable for real time applications. To improve the accuracy of the system we have applied 2DPCA on LBP images in place of original images .. The comparative analysis of both methods is done on the basis of their recognition accuracy and time complexity through experimental results. The proposed system has achieved the recognition rate of 95.12% for 2DPCA and 95.83 % for LBP+2DPCA. The time required to recognize an expression for 2DPCA is very less as compared to other contemporary methods.
A novel method is proposed for facial expression recognition. We have implemented two techniques for automatic facial expression recognition. First, we applied transfer learning to AlexNet, and VGG19 for classification. Second, we used AlexNet and Vgg19 for feature extraction and cascaded it with an SVM for classification. We achieved 86.11% accuracy with AlexNet and 94.44% with AlexNet-SVM cascade. We also achieved 94.44% accuracy with VGG19 and 86.11 with VGG19-SVM cascade. We used JAFFE Data Set to train our four models. Our system achieves an improvement in accuracy for JAFFE Data Set.
Identification of the denomination of the currency note to pay physically without UPI is the first step of paying to the seller by the consumer. In this project, we have proposed an approach to detect denominations of Indian currency using Convolutional Neural Networks. Computer Vision and object detection is an area of great interest for research in today’s world. It has several applications like detection of defects in machinery, intruder detection, computer vision for code and character recognition among many others. Through the work we have done, we explored something that could be of great help to people in day-to-day life. In this project we have tried to investigate the approaches to detect currency denominations using Convolutional Neural Networks. The objective is to build a model that would be able to detect Indian currency denominations efficiently. Typically the model will be useful for people with vision impairment. The experimental results show that the use of Convolutional Neural Networks is a good way and the model can further be improved if it is trained in such a way that it could also identify the regions of interest.
Vegetation is an essential part of our ecosystem; it also determines health of our planet. According to “The State of the World’s Forests 2020” only 31% of the global land area is covered with vegetation. This paper presents a superior way of representing vegetation cover in a region by utilizing Atmospherically Resistant Vegetation Index (ARVI) as vital features out of multispectral satellite images. These images comprise Green, Blue, Red and Near Infrared bands data which was further utilized by U-Net to efficiently segment satellite images. In remote sensing greenery of environment is traditionally determined using NDVI, one of the critical imperfections with this method is that it is liable to compute inaccurate values as a consequence of variations in soil, air moisture and shadowing affected by varying incidence angle of sunlight. On the other hand, ARVI is immune to such flaws and integration with deep learning provides a better solution for segmenting regional vegetation and predicting its coverage up to some extent. This method can be employed for predicting vegetation in any region along with assisting in events such as repopulating trees and urban planning while conserving beauty of our nature.
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