mjay4 Goto Github PK

automation

Automating file transfer using python.

competitive-coding

Contains code for my reference.

covid-everything

It was a Hackathon Project organised by HackJaipur Team. It's solely for Educational Purpose. It shows all latest updates related to COVID-19

cpu-scheduling

FCFS, Round Robin method are only finished for now.

cryptochain

Build a blockchain-based cryptocurrency on the full stack course

dollartoinr

Sample App To Convert Dollar To INR using a Free API to get the current value Dynamically.

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

face-dataset

Face related datasets

face-detecion-with-the-faster-r-cnn-r-fcn

Face detection with the Faster-R-CNN or R-FCN

face-py-faster-rcnn

Face Detection with the Faster R-CNN

face_recognition

The world's simplest facial recognition api for Python and the command line

facebook-hacker-cup-2020-qualification-round

This is the repository to hold my solutions for Facebook Hacker Cup 2020 Qualification Round, I secured 2664/ 32699 Rank. It was my first faceBook competition and I learned a lot. Please ping me if u can optimize my solution at : [email protected]

hackerrank

HackerRank solutions in Java/JS/Python/C++/C#

hackerrankproblems

This repository contains the solution of HackerRank Problems.

insightface

Face Analysis Project on MXNet

interviews

Everything you need to know to get the job.

java-a-z

Java programming. Join the Discord link.

leetcode-questions

Leetcode question list by companies, includes the premium questions. December 2019 updated

logicgate

My first ML implementation of "AND Gate" using linear Regression.

machinelearning-ai-projects

This file contains all the small and big projects that I am doing on AI and MachineLearning.

mjay4.github.io

This is my Portfolio.

mtcnn

MTCNN face detection implementation for TensorFlow, as a PIP package.

mxnet_mtcnn_face_detection

MTCNN face detection

odinproject

opencv

Open Source Computer Vision Library

opencv-face-recognition

Real-time face recognition project with OpenCV and Python

poducerandconsumer

C program using semaphore to show the parallel execution of the Producer Consumer scenario.

refinedet

Single-Shot Refinement Neural Network for Object Detection, CVPR, 2018

speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

srn

Selective Refinement Network for High Performance Face Detection, AAAI, 2019