Pinned Repositories
ccminer
CUDA miner project, compatible with most nvidia cards
Codenameone_Demo
Demo project using Codenameone Framework
Collage-Management-System
This is a Project based on a Collage Management System
Country_Need_a_Leader
cpuminer
crypto cpuminer (linux + windows)
E-Shipping-System
E-Shipping Service
emotion-conv-net
Real-time emotion recognition using convolutional neural nets.
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.
emotion-recognition-neural-networks
Emotion recognition using DNN with tensorflow
Whatsapp-Decryptor
This project help user to decrypt the .crypt8 Whatspp database to view it.
jugrajsingh's Repositories
jugrajsingh/Whatsapp-Decryptor
This project help user to decrypt the .crypt8 Whatspp database to view it.
jugrajsingh/E-Shipping-System
E-Shipping Service
jugrajsingh/ccminer
CUDA miner project, compatible with most nvidia cards
jugrajsingh/Codenameone_Demo
Demo project using Codenameone Framework
jugrajsingh/Collage-Management-System
This is a Project based on a Collage Management System
jugrajsingh/Country_Need_a_Leader
jugrajsingh/cpuminer
crypto cpuminer (linux + windows)
jugrajsingh/emotion-conv-net
Real-time emotion recognition using convolutional neural nets.
jugrajsingh/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.
jugrajsingh/emotion-recognition-neural-networks
Emotion recognition using DNN with tensorflow
jugrajsingh/Emotion-Recognition-Using-SVMs
A software which detect a human face through live webcam feed and identifies the emotion of the person (i.e. the person is happy or sad).
jugrajsingh/emrec
Emotion Recogniser
jugrajsingh/glpi
GLPI is a Free Asset and IT Management Software package, Data center management, ITIL Service Desk, licenses tracking and software auditing.
jugrajsingh/GndecSMS
Android Application for SMS Service
jugrajsingh/jugrajsingh
Config files for my GitHub profile.
jugrajsingh/mememoji
A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.
jugrajsingh/papermark
Papermark is the open-source DocSend alternative with built-in analytics and custom domains.
jugrajsingh/Picture-Puzzle
test upload
jugrajsingh/platform_development
jugrajsingh/pydeeplearn
Deep learning API with emotion recognition application
jugrajsingh/sgminer-gm
A multi-algo GPU miner
jugrajsingh/shelf.nu
shelf is open source Asset Management Infrastructure for absolutely everyone.
jugrajsingh/supabase-kubernetes
Helm 3 charts to deploy a Supabase on Kubernetes
jugrajsingh/Survey_App
It is a cross platform app development project for civil survey data collection.
jugrajsingh/vaultwarden_heroku
Vaultwarden (Bitwarden Rust implementation) self hosted in Heroku for Free!
jugrajsingh/wazuh-kubernetes
Wazuh - Wazuh Kubernetes
jugrajsingh/xmr-stak
Unified All-in-one Monero miner
jugrajsingh/xmrig
Monero (XMR) CPU miner