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This notebook is trying to build a model which will predict Bollywood celebrity the given image looks like using ResNet-50. This project is trained on 100 Bollywood celebrities and the dataset is taken from kaggle.
Python script to detect and extract faces from images to create specific datasets. It is based on the MTCNN Library which is an implementation of the ZHANG2016 research paper.
A Gradio-based web application that detects whether an image is a deepfake. The application uses a pre-trained InceptionResnetV1 model from the facenet_pytorch library for face recognition, and pytorch-grad-cam for visual explainability.
A research work on human emotion analysis form the images focussing on the four emotions (Happy, Sad, Surprised and Angry). CNN, MTCNN and Haarcascade algorithms are used in this research.
Developed a working prototype of a University Security Monitoring System using Deep Learning to assist Reva University in maintaining a database of known and unknown persons entering through the entrance gate by detecting the frontal faces of students.
Facial Expression Classifier using CNN models trained on FER2013 dataset, implementation of opencv in face detection and expression recognition using the trained model as well as tutorials regarding the same.
This project involves training Google's MobileNet V2 CNN model on a dataset of masked, incorrectly masked and unmasked images in order to perform face mask detection