Suraj Ravishankar
Student Ambassador

Suraj Ravishankar

Tempe, AZ, USA

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Suraj R. added photos to project Music Recommender based on Emotion determined by Facial Expression

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Music Recommender based on Emotion determined by Facial Expression

Emotions are the essence of what makes us human. They impact our daily routines, our social interactions, our attention, perception, and memory. One of the strongest indicators for emotions is our face. As we laugh or cry we’re putting our emotions on display, allowing others to glimpse into our minds as they "read" our face based on changes in key face features.

Facial expressions refer to movements of the mimetic musculature of the face. The vast majority of these muscles are innervated by the cranial nerve, emanating from the brainstem between the pons and medulla. The nerve includes a motor root that supplies somatic muscle fibers to the muscles of the face, scalp, and outer ear, enabling the muscle movements that comprise facial expressions.

Computer-based facial expression recognition is a challenging task in computer vision as it captures raw, unfiltered emotional responses towards any type of emotionally engaging content.

Here we are going to determine (successfully) your mood and play music based on your determined emotion.

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Suraj R. created project Music Recommender based on Emotion determined by Facial Expression

Medium c072ba68 ce04 48cc 8357 4d956daaad03

Music Recommender based on Emotion determined by Facial Expression

Emotions are the essence of what makes us human. They impact our daily routines, our social interactions, our attention, perception, and memory. One of the strongest indicators for emotions is our face. As we laugh or cry we’re putting our emotions on display, allowing others to glimpse into our minds as they "read" our face based on changes in key face features.

Facial expressions refer to movements of the mimetic musculature of the face. The vast majority of these muscles are innervated by the cranial nerve, emanating from the brainstem between the pons and medulla. The nerve includes a motor root that supplies somatic muscle fibers to the muscles of the face, scalp, and outer ear, enabling the muscle movements that comprise facial expressions.

Computer-based facial expression recognition is a challenging task in computer vision as it captures raw, unfiltered emotional responses towards any type of emotionally engaging content.

Here we are going to determine (successfully) your mood and play music based on your determined emotion.

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Suraj R. created project Vehicle Detection

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Vehicle Detection

Detecting vehicles in a video stream is an object detection problem. An object detection problem can be approached as either a classification problem or a regression problem. In the classification approach, the image are divided into small patches, each of which will be run through a classifier to determine whether there are objects in the patch. The bounding boxes will be assigned to patches with positive classification results. In the regression approach, the whole image will be run through a convolutional neural network directly to generate one or more bounding boxes for objects in the images.

The goal of this project is to detect the vehicles in a camera video. The You Only Look Once (YOLO) algorithm is used here to detect the vehicles from a dash camera video stream. This feature is an extremely important breakthrough for self-driving cars as we can train the model to also recognize birds, people, stop signs, signals and much more.

In this project, we will implement the version 1 of tiny-YOLO in Keras, since it’s easy to implement and is reasonably fast.

The YOLO approach of the object detection is consists of two parts: the neural network part that predicts a vector from an image, and the postprocessing part that interpolates the vector as boxes coordinates and class probabilities. For the neural network, the tiny YOLO v1 is consist of 9 convolution layers and 3 full connected layers. Each convolution layer consists of convolution, leaky relu and max pooling operations. The output of this network is a 1470 vector, which contains the information for the predicted bounding boxes. The 1470 vector output is divided into three parts, giving the probability, confidence and box coordinates.

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Suraj R. created project Human Activity Recognition

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Human Activity Recognition

Human Activity Recognition which use sensors to recognize human actions, have been studied for a long time to produce a simpler system with higher precision. However, there are a very limited number of projects that investigate a human activity recognition system built right on the smartphone. A great advantage of this integrated system is the real time and full time supervision. The human activities recognition built right in a smart phone promises to open up a new direction not only in monitoring and health care but also in other fields. Smartphones are going to get more popular in the world over the next five years. According to Ericsson's mobility report, there will be a massive jump from the 2.6 billion smartphone users recorded in 2014 to 6.1 billion by 2020. These smartphones are also equipped with a lot of sensors. In detail, accelerometer sensor measures acceleration in three orthogonal axes. All of objects in the Earth are affected by the gravity. The linear acceleration measures the acceleration effect of the device movement, excluding the effect of Earth's gravity on the device. The gyroscope uses Earth’s gravity to help determine orientation of smartphone. Thus, the idea of utilization of these sensors to make a smartphone application for human activities recognition became more realistic. Android is the most popular mobile OS in the world with more than 82% phones running on it and with Google’s continuously improving features by listening to feedback from its users, it is only going to get more popular. In Android, Google has introduced a set of APIs (Application Programming Interface), which allow the developer to connect Google services to their Android phone for receiving the human activity recognition results. Google API can recognize six type of activities which include “in vehicle”, “on bicycle”, “on foot”, “running”, “still” and “walking”. However, this system required the connection to Google server inorder-to send the requests and receive the results.

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Suraj R. updated status

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Suraj Ravishankar

Working to build an Android application which recognizes and classifies human activities using machine learning techniques. To recognize the activities, gathering data sets related to human actions in daily life would be required. Nowadays, most people have their smartphone and the smartphones have many sensors such as accelerometer, gyroscope, orientation, GPS, proximity, etc. By using these sensors, we can obtain the datasets required for identifying human movements.

Have applied and trying to become an Intel Student Ambassador as part of the Intel Student Developer Program.

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