Posts in Agra | India

This project aims to classify a group’s perceived emotion as Positive, Neutral or Negative. The dataset being used is the Group Affect Database 3.0 which contains "in the wild" photos of groups of people in various social environments.The Need for Emotion Recognition So, first of all, why do we need emotion recognition? Emotion recognition is important - - To improve the user’s experience, as a customer, learner, or as a generic service user. - Can help improve services without the need to formally and continuously ask the user for feedback. - Also, using automatic emotion recognition in public safety, healthcare, or assistive technology, can significantly improve the quality of people’s lives, allowing them to live in a safer environment or reducing the impact that disabilities or other health conditions have.Applications of Emotion Recognition Emotion Recognition has applications in crowd analytics, social media, marketing, event detection and summarization, public safety, human-computer interaction, digital security surveillance, street analytics, image retrieval, etc.The rise of Group Emotion Recognition The problem of emotion recognition for a group of people has been less extensively studied, but it is gaining popularity due to the massive amount of data available on social networking sites containing images of groups of people participating in social events.Challenges facing Group Emotion Recognition Group emotion recognition is a challenging problem due to obstructions like head and body pose variations, occlusions, variable lighting conditions, variance of actors, varied indoor and outdoor settings and image quality.Approach My approach is based on the research paper "Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers". So, the model is basically a novel combination of deep neural networks and Bayesian classifiers. The neural network works from the bottom to the top, analysing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. Top-down approach considers the scene context, such as background, clothes, place, etc. It consists of the following steps – - Acquiring the scene descriptors - Setting evidences in the Bayesian Network - Estimating the posterior distribution of the Bayesian Network Bottom-up approach estimates the facial expressions of each person in the group – - Face detection - Features pre-processing - CNN forward pass The value obtained by the bottom-up module is then used as input to the Bayesian Network in the top layer.

In this app students or the user can read our latest blog and updates and take a daily quiz every day and increase English skills, the user also read every day new word and add in his vocabulary we update every day a topper of scholar students data. All program run by Mr. Raju Gola his a great personality and a great motivational speaker. you can also visit our official blog www.scholars.net.in.

A shorcut to data usage settings. Disable background data. Useful for coolpad users.

In Release Me, you will control our round hero (a simple football really) and try to release the locked power cube(which is just a green colored cube) in a variety of stage settings and under a limited time.Along the way, you will encounter various enemies(which are actually moving cubes) and tread along treacherous paths and moving obstacles which will kill you in an instant hit. You can collect tokens to increase score and a freeze cube to freeze all the enemies for a limited time(handy, isn't it), and a key to unlock the cage(very important, can't win without it), and release and eventually collect the power cube.Features:• Fluid, graceful and exhilarating physics-based game play.• 22 addictive stages• Enemy with plain as well as erratic movements• Easy to learn, difficult to master.• Collect tokens in hard to reach places.• One Touch Death• Camera which follows the player• Countdown timer for each stage.• Beautifully minimalist visual designDisclaimer: We have credited each of the assets owner, whose wonderful contributions we have used in the game itself. If by any chance, you do not see your name in the credits, please do mail us at appgalleryofficial@gmail.com. We will be sure to include it in the due credits.