An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions
20 Aug 2014-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 100, Iss: 9, pp 33-39
TL;DR: An algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually is presented.
Abstract: Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This paper presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed in this paper aspires to reduce the overall computational time and the cost of the designed system. It also aims at increasing the accuracy of the designed system. The facial expression recognition module of the proposed algorithm is validated by testing the system against user dependent and user independent dataset. Experimental results indicate that the user dependent results give 100% accuracy, while user independent results for joy and surprise are 100 %, but for sad, anger and fear are 84.3 %, 80 % and is 66% respectively. The overall accuracy of the emotion recognition algorithm, for user independent dataset is 86%. In audio, 100 % recognition rates are obtained for sad, sad-anger and joy-anger but for joy and anger, recognition rates obtained are 95.4% and 90 % respectively. The overall accuracy of the audio emotion recognition algorithm is 98%. Implementation and testing of the proposed algorithm is carried out using an inbuilt camera. Hence, the proposed algorithm reduces the overall cost of the system successfully. Also, on average, the proposed algorithm takes 1.10 sec to generate a playlist based on facial expression. Thus, it yields better performance, in terms of computational time, as compared to the algorithms in the existing literature.
Citations
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TL;DR: A quick survey of facial expression recognition is presented and a comparative study is also carried out using various feature extraction techniques on JAFFE dataset.
141 citations
01 Sep 2019
40 citations
Institute of High Performance Computing Singapore1, National University of Singapore2, Leiden University3, Utrecht University4, Open University5, Zuyd University of Applied Sciences6, Royal Institute of Technology7, University of Erlangen-Nuremberg8, Singapore University of Technology and Design9, Pompeu Fabra University10, Delft University of Technology11, Queen Mary University of London12, Temple University13
TL;DR: The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being as discussed by the authors. In an effort to strengthen the collabor...
Abstract: The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collabor...
33 citations
Cites background from "An Accurate Algorithm for Generatin..."
...A similar system by Dureha (2014) creates both mood-uplifting as well as mood-stabilizing playlists....
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TL;DR: This work has proposed deep-learning framework which consist of CNN, ResNet and attention block which gives visual perceptibility to the network and has greater applicability in real life facial emotion detection.
18 citations
TL;DR: The experimental results demonstrate that the proposed cascade regression-based face frontalization (CRFF) method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition.
Abstract: Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses To address these issues, this paper presents a cascade regression-based face frontalization (CRFF) method, which aims to immediately reconstruct a clean, frontal and expression-aware face given an in-the-wild facial image In the first stage, a frontal facial shape is predicted by developing a cascade regression model to learn the pairwise spatial relation between non-frontal face-shape and its frontal counterpart Unlike most existing shape prediction methods that used single-step regression, the cascade model is a multi-step regressor that gradually aligns non-frontal shape to its frontal view We employ several different regressors and make a ensemble decision to boost prediction performance For facial texture reconstruction, active appearance model instantiation is employed to warp the input face to the predicted frontal shape and generate a clean face To remove occlusions, we train this generative model on manually selected clean-face sets, which ensures generating a clean face as output regardless of whether the input face involves occlusions or not Unlike the existing face reconstruction methods that are computational expensive, the proposed method works in real time, so it is suitable for dynamic analysis of facial expression The experimental validation shows that the ensembling cascade model has improved frontal shape prediction accuracy for an average of 5% and the proposed method has achieved superior performance on both static and dynamic recognition of facial expressions over the state-of-the-art approaches The experimental results demonstrate that the proposed method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition
17 citations
Cites background from "An Accurate Algorithm for Generatin..."
...Facial expression recognition (FER) has a wide range of applications including human–computer interaction (HCI) [38, 6], animation [1, 36, 21] and security [24]....
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References
More filters
12,519 citations
01 Jan 1978
3,840 citations
TL;DR: In this paper, a wide variety of extensions have been made to the original formulation of the Lucas-Kanade algorithm and their extensions can be used with the inverse compositional algorithm without any significant loss of efficiency.
Abstract: Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision Applications range from optical flow and tracking to layered motion, mosaic construction, and face coding Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed We examine which of the extensions to Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters
3,168 citations
TL;DR: In this paper, the authors discuss human emotion perception from a psychological perspective, examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data.
Abstract: Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.
2,503 citations
14 Apr 1998
TL;DR: The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage and the Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfaces.
Abstract: A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage. The Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfaces.
2,100 citations