FaceFetch: A User Emotion Driven Multimedia Content Recommendation System Based on Facial Expression Recognition

Mariappan, Mahesh Babu; Suk, Myunghoon; Prabhakaran, Balakrishnan

doi:10.1109/ism.2012.24

Cited by 21 publications

(7 citation statements)

References 7 publications

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“…In the future work, the proposed system may be integrated with a variety of mobile applications or systems without any remote servers (e.g., the system recommending multimedia content in [15])…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Mobile Facial Expression Recognition System -- A Case Study

Suk

Prabhakaran

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Self Cite

115

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This paper presents a mobile application for real time facial expression recognition running on a smart phone with a camera. The proposed system uses a set of Support Vector Machines (SVMs) for classifying 6 basic emotions and neutral expression along with checking mouth status. The facial expression features for emotion recognition are extracted by Active Shape Model (ASM) fitting landmarks on a face and then dynamic features are generated by the displacement between neutral and expression features. We show experimental results with 86% of accuracy with 10 folds cross validation in 309 video samples of the extended Cohn-Kanade (CK+) dataset. Using the same SVM models, the mobile app is running on Samsung Galaxy S3 with 2.4 fps. The accuracy of real-time mobile emotion recognition is about 72% for 6 posed basic emotions and neutral expression by 7 subjects who are not professional actors.

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Section: Discussionmentioning

confidence: 99%

“…But they showed experiment results with only 4 classes such as sadness, surprise, neutral, and happiness. In [15], authors proposed a system recommending multimedia content to user by understanding a user's current emotional state.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Mobile Facial Expression Recognition System -- A Case Study

Suk

Prabhakaran

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Self Cite

115

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“…The sources of knowledge are usually represented by three types of descriptors for: items, users and transactions (relations between user and item). Modern recommendation systems also use textual reviews [20], images [58], web page sequences [62], user emotion (Facial Expression Recognition [41] or even text reviews [19]), images and web page sequences, and processed through data mining or deep learning methods, to generate recommendations.…”

Section: Related Workmentioning

confidence: 99%

Multi Clustering Recommendation System for Fashion Retail

Bellini

Palesi

Nesi

et al. 2022

Multimed Tools Appl

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Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online.

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“…Ancak insanların halihazırda kullandığı bu sistemler duygularımızı işin içine katmamaktadır. Duyguları da dikkate alarak kullanıcılara daha uygun öneriler sunulabilir (Babanne, Borgaonkar, Katta, Kudale, & Deshpande, 2020;Mariappan, Suk, & Prabhakaran, 2012).…”

Section: Introductionunclassified

Yüz İfadesini Algılayarak Kullanıcının Ruh Haline Göre İçerik Öneren Mobil Uygulama

Gündüz¹,

Yılmaz²

2021

European Journal of Science and Technology

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ÖzGünümüzde insanlar, boş zamanlarını değerlendirmek için akıllı cihazları üzerinden dijital içerikler tüketmektedir. Ancak bazen kullanıcılar o anki ruh durumlarına göre bir film, dizi veya video izlemek ya da müzik dinlemek isteyebilir. Bu çalışmada, bu soruna çözüm olarak geliştirilen mobil uygulama anlatılmaktadır. Hedef cihaz olarak da hemen hemen herkesin sahip olduğu akıllı telefonlar seçilmiştir. Mobil uygulamada, kullanıcı rastgele olarak veya o anki ruh durumunu kendisi belirterek veya kullanıcının yüz ifadesine göre içerik önerileri alabilir. Yüz ifadesine göre içerik önerisinde bulunmak amacıyla, yapay zeka ve görüntü işleme kullanılarak, akıllı telefonun kamerasından kullanıcının anlık yüz ifadesi elde edilerek kullanıcının ruh hali tahmin edilir ve kullanıcıya bu ruh haline uygun dijital içerik önerilir. Ayrıca, hangi duyguya karşılık hangi içerik türünün içinden seçim yapılacağı, sistem yöneticisi tarafından belirlenebilir.

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FaceFetch: A User Emotion Driven Multimedia Content Recommendation System Based on Facial Expression Recognition

Cited by 21 publications

References 7 publications

Real-Time Mobile Facial Expression Recognition System -- A Case Study

Real-Time Mobile Facial Expression Recognition System -- A Case Study

Multi Clustering Recommendation System for Fashion Retail

Yüz İfadesini Algılayarak Kullanıcının Ruh Haline Göre İçerik Öneren Mobil Uygulama

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