VGGFace implementation with Keras Framework

PyTorch Face Recognizer based on 'VGGFace2: A dataset for recognising faces across pose and age'

Age and Gender Estimation Using Convolutional Neural Network

This is a very Simple example of showing how to build image dataset from your own collection of images, how to train multiple class classifier using tensorflow CNN and how to predict the class of an object in an unseen image. This code is designed so that a newbie user become able to train a tensorflow CNN model using his limited CPU resources. …

Face Detection, verification and recognition in Near real time on CPU brewing with all the SOTA all over

A flask app to give a demo of Facial Recognition - Deployed with Live Demo.

8 emotions detected in real-time with ~77% accuracy. Used: OpenCV, Python 3, Keras, Data Preprocessing, Deep Learning & Machine learning Techniques.

EVERYTHING YOU NEED FOR DATA SCIENCE.

Continuous Face Authentication on Android

Train/validate VGGface2 dataset based on L2-constrained softmax loss.

Utilizing the DeepFace Library, informed by a dataset of 4M images across 4K identities curated by Facebook researchers, My 'Two Faces✌🏻' project gauges facial similarity with precision.

Model to recognize celebrities using a face matching algorithm.

Facial recognition using pretrained VGGFace model to compare faces from images and recognise/match them.

Data and code for an AI model that predicts remaining lifespan (how many years of life a person has left) solely from a facial image

A containerized facial recognition module based on VGG ResNet-50 architecture

Facial Recognition with VGG Face in Tensorflow 2.0

Used VGGFace and Resnet50 model we extract features from the photos of the celebrities. It predicts the similar matching Bollywood celeb based on cosine similarity of the pictures.

RealTime Emotion Recognizer for Machine Learning Study Jam's demo

ML model for grouping similar faces using cutting-edge deep learning and computer vision techniques. Custom dataset of 300 images captures comprehensive facial variations. Siamese network outperforms Face-Net, delivering reliable clustering results.

This is my capstone project that was completed as part of the General Assembly Data Science Immersive curriculum. I conceptualized the idea, executed the project and developed a prototype for predicting success in the South Korean drama industry based on face image. This serves as a proof-of-concept which can definitely be developed further.