85 research outputs found
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields
Automated Facial Expression Recognition (FER) has been a challenging task for
decades. Many of the existing works use hand-crafted features such as LBP, HOG,
LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as
Support Vector Machines for expression recognition. These methods often require
rigorous hyperparameter tuning to achieve good results. Recently Deep Neural
Networks (DNN) have shown to outperform traditional methods in visual object
recognition. In this paper, we propose a two-part network consisting of a
DNN-based architecture followed by a Conditional Random Field (CRF) module for
facial expression recognition in videos. The first part captures the spatial
relation within facial images using convolutional layers followed by three
Inception-ResNet modules and two fully-connected layers. To capture the
temporal relation between the image frames, we use linear chain CRF in the
second part of our network. We evaluate our proposed network on three publicly
available databases, viz. CK+, MMI, and FERA. Experiments are performed in
subject-independent and cross-database manners. Our experimental results show
that cascading the deep network architecture with the CRF module considerably
increases the recognition of facial expressions in videos and in particular it
outperforms the state-of-the-art methods in the cross-database experiments and
yields comparable results in the subject-independent experiments.Comment: To appear in 12th IEEE Conference on Automatic Face and Gesture
Recognition Worksho
Implementasi Convolutional Neural Networks (CNN) untuk Klasifikasi Ekspresi Citra Wajah pada FER-2013 Dataset
Abstract - session recognition is an interesting topic, where facial expressions in today's technological advances can support several fields such as health, business, and so on. Facial expression recognition can be done using the extraction of certain features. Meanwhile, Convolutional Neural Network (CNN) can recognize an object in the image through the features found by itself in the convolution process. By using CNN's advantages, this study aims to see CNN's performance in facial expressions of happiness and sadness in unideal data conditions. So based on this research, on the FER2013 dataset, CNN using the Adamax optimizer produced a fairly good performance where the value is given is 66% compared to Adam, N-Adam, and SGD.Keywords  -  CNN, Facial Expression, FER-2013 Abstrak – Pengenalan ekspresi merupakan topik penelitian yang menarik, dimana peran ekspresi wajah dalam kemajuan teknologi saat ini dapat mendukung beberapa bidang seperti kesehatan, bisnis, dan sebagainya. Pengenalan wajah dapat dilakukan dengan menggunakan ekstraksi fitur-fitur tertentu. Sementara itu, Convolutional Neural Network (CNN) dapat mengenali objek pada citra melalui fitur yang ditemukannya sendiri dalam proses konvolusinya. Dengan menggunakan keunggulan CNN, maka penelitian ini bertujuan untuk mengetahui performa CNN dalam mengenali ekspresi wajah bahagia (happy) dan sedih (sad) pada kondisi data tidak ideal. Maka berdasarkan hasil penelitian ini, pada dataset FER2013, CNN dengan menggunakan Adamax optimizer menghasilkan performa yang cukup baik dimana akurasi yang diberikan adalah sebesar 66% dibandingkan dengan Adam, N-Adam, dan SGD.Kata Kunci - CNN, Ekspresi Wajah, FER-2013
Mirror Ritual: Human-Machine Co-Construction of Emotion
Mirror Ritual is an interactive installation that challenges the existing
paradigms in our understanding of human emotion and machine perception. In
contrast to prescriptive interfaces, the work's real-time affective interface
engages the audience in the iterative conceptualisation of their emotional
state through the use of affectively-charged machine generated poetry. The
audience are encouraged to make sense of the mirror's poetry by framing it with
respect to their recent life experiences, effectively `putting into words'
their felt emotion. This process of affect labelling and contextualisation
works to not only regulate emotion, but helps to construct the rich personal
narratives that constitute human identity.Comment: Paper presented at ACM TEI Conference 2020 Arts Track, Sydney
Australi
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
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