International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 141 - Issue 4 |
Published: May 2016 |
Authors: Abhishek R. Patel, Anusha Vollal, Pradnyesh B. Kadam, Shikha Yadav, Rahul M. Samant |
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Abhishek R. Patel, Anusha Vollal, Pradnyesh B. Kadam, Shikha Yadav, Rahul M. Samant . MoodyPlayer: A Mood based Music Player. International Journal of Computer Applications. 141, 4 (May 2016), 21-25. DOI=10.5120/ijca2016909598
@article{ 10.5120/ijca2016909598, author = { Abhishek R. Patel,Anusha Vollal,Pradnyesh B. Kadam,Shikha Yadav,Rahul M. Samant }, title = { MoodyPlayer: A Mood based Music Player }, journal = { International Journal of Computer Applications }, year = { 2016 }, volume = { 141 }, number = { 4 }, pages = { 21-25 }, doi = { 10.5120/ijca2016909598 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2016 %A Abhishek R. Patel %A Anusha Vollal %A Pradnyesh B. Kadam %A Shikha Yadav %A Rahul M. Samant %T MoodyPlayer: A Mood based Music Player%T %J International Journal of Computer Applications %V 141 %N 4 %P 21-25 %R 10.5120/ijca2016909598 %I Foundation of Computer Science (FCS), NY, USA
Increasing and maintaining human productivity of different tasks in stressful environment is a challenge. Music is a vital mood controller and helps in improving the mood and state of the person which in turn will act as a catalyst to increase productivity. Continuous music play requires creating and managing personalized song playlist which is a time consuming task. It would be very helpful if the music player itself selects a song according to the current mood of the user. The mood of the user can be detected by a facial expression of the person. A facial expression detection system should address three major problems: detection of face from an image, facial feature extraction and facial expression classification[1].The first stage is of face detection from an image for which various techniques used are model based face tracking which includes real-time face detection using edge orientation matching [2], Robust face detection using Hausdorff distance [3], weak classifier cascade which includes Viola and Jones algorithm [4], and Histograms of Oriented Gradients (HOG) descriptors. The next stage is to extract features from detected face. Two major approaches for feature extraction which use Gabor filters [Dennis Gabor] and Principle Component Analysis [Jolliffe]. The final stage is of image classification for mood detection, where various classifiers like BrownBoost [Freund, 2001], AdaBoost [Freund and Schapire, 1995] and Support Vector Machines (SVM) are available. The proposed system will use classic Histograms of Oriented Gradients (HOG) along with facial landmark detection technique; these detected features then passed through SVM classifier to predict the mood of the user. This predicted mood will stimulate the creation of playlist.