In recent years, fingerprint-based positioning has gained researchers attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas.
This paper provides a comprehensive review of deep learning methods in indoor positioning.
Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment.
Ranked #7 on Face Alignment on COFW
In addition, the Mean Discriminator component leads the network to make the mean embedded feature vectors of different classes to be less similar to each other. We use Xception network as the backbone of our model, and contrary to previous work, we propose an embedding feature space that contains k feature vectors.
Ranked #7 on Facial Expression Recognition on RAF-DB
To address the computational burdens of the Dynamic Routing mechanism, this paper proposes new Fully Connected (FC) layers by xnorizing the linear projector outside or inside the Dynamic Routing within the CapsFC layer.
Ranked #12 on Image Classification on MNIST (Accuracy metric)
We use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network.
Ranked #8 on Face Alignment on COFW
In order to meet the need for a high quality, publicly available male speech corpus within the field of speech recognition, we have designed and created RyanSpeech which contains textual materials from real-world conversational settings.
We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and topical language generation probability as the posterior.
We conducted comprehensive experiments on the categorical and dimensional models of affect on the challenging in-the-wild databases of AffectNet, FER2013, and Affect-in-Wild.
Social robots are becoming an integrated part of our daily life due to their ability to provide companionship and entertainment.
This paper introduces Bounded Residual Gradient Networks (BReG-Net) for facial expression recognition, in which the shortcut connection between the input and the output of the ResNet module is replaced with a differentiable function with a bounded gradient.
This paper discovers how the performance and accuracy of automated behavior recognition from the LFP signals are affected under different paradigms of stimulation on/off.
AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models.
Ranked #16 on Facial Expression Recognition on AffectNet
Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER).
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.
Classification of human behavior is key to developing closed-loop Deep Brain Stimulation (DBS) systems, which may be able to decrease the power consumption and side effects of the existing systems.
Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop.
In fact, the Internet is a Word Wild Web of facial images with expressions.
Active Appearance Model (AAM) is a commonly used method for facial image analysis with applications in face identification and facial expression recognition.
Despite efforts made in developing various methods for FER, existing approaches traditionally lack generalizability when applied to unseen images or those that are captured in wild setting.