1,454 research outputs found
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
Smile detection in the wild based on transfer learning
Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amounts of image data, while being robust to noise and other
undesired artifacts. Potentially, this makes VAEs a suitable approach for
learning facial features for AU intensity estimation. Yet, most existing
VAE-based methods apply classifiers learned separately from the encoded
features. By contrast, the non-parametric (probabilistic) approaches, such as
Gaussian Processes (GPs), typically outperform their parametric counterparts,
but cannot deal easily with large amounts of data. To this end, we propose a
novel VAE semi-parametric modeling framework, named DeepCoder, which combines
the modeling power of parametric (convolutional) and nonparametric (ordinal
GPs) VAEs, for joint learning of (1) latent representations at multiple levels
in a task hierarchy1, and (2) classification of multiple ordinal outputs. We
show on benchmark datasets for AU intensity estimation that the proposed
DeepCoder outperforms the state-of-the-art approaches, and related VAEs and
deep learning models.Comment: ICCV 2017 - accepte
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