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

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%

Heegard-Berger and Cascade Source Coding Problems with Common Reconstruction Constraints

For the HB problem with the CR constraint, the rate-distortion function is derived under the assumption that the side information sequences are (stochastically) degraded. The rate-distortion function is also calculated explicitly for three examples, namely Gaussian source and side information with quadratic distortion metric, and binary source and side information with erasure and Hamming distortion metrics. The rate-distortion function is then characterized for the HB problem with cooperating decoders and (physically) degraded side information. For the cascade problem with the CR constraint, the rate-distortion region is obtained under the assumption that side information at the final node is physically degraded with respect to that at the intermediate node. For the latter two cases, it is worth emphasizing that the corresponding problem without the CR constraint is still open. Outer and inner bounds on the rate-distortion region are also obtained for the cascade problem under the assumption that the side information at the intermediate node is physically degraded with respect to that at the final node. For the three examples mentioned above, the bounds are shown to coincide. Finally, for the HB problem, the rate-distortion function is obtained under the more general requirement of constrained reconstruction, whereby the decoder's estimate must be recovered at the encoder only within some distortion.Comment: to appear in IEEE Trans. Inform. Theor

The Effects of Different Roughness Configurations on Aerodynamic Performance of Wind Turbine Airfoil and Blade

In this research, viscous and turbulent flow is simulated numerically on an E387 airfoil as well as on a turbine blade. The main objective of this paper is to investigate various configurations of roughness to find a solution in order to mitigate roughness destructive impacts. Hence, the sand grain roughness is distributed uniformly along pressure side, suction side and both sides during the manufacturing process. Navier-Stokes equations are discretized by the finite volume method and are solved by SIMPLE algorithm. Results indicated that in contrast with previous studies, the roughness will be useful if it is applied on only pressure side of the airfoil. In this condition, the lift coefficient is increased to and 1.2% compare to the airfoil with rough and smooth sides, respectively. However, in 3-D simulation, the lift coefficient of the blade with pressure surface roughness is less than smooth blade, but still its destructive impacts are much less than of both surfaces roughness and suction surfaces roughness. Therefore, it can be deduced that in order to reveal the influence of roughness, the simulation must be accomplished in three dimensions.Article History: Received Jun 12th 2017; Received in revised form August 27th 2017; Accepted Oct 3rd 2017; Available onlineHow to Cite This Article: Jafari, K., Djavareshkian, M.H., Feshalami, B.H. (2017) The Effects of Different Roughness Configurations on Aerodynamic Performance of Wind Turbine Airfoil and Blade. International Journal of Renewable Energy Develeopment, 6(3), 273-281.https://doi.org/10.14710/ijred.6.3.273-28

Entanglement Capacity of Nonlocal Hamiltonians : A Geometric Approach

We develop a geometric approach to quantify the capability of creating entanglement for a general physical interaction acting on two qubits. We use the entanglement measure proposed by us for $N$-qubit pure states (PRA \textbf{77}, 062334 (2008)). Our procedure reproduces the earlier results (PRL \textbf{87}, 137901 (2001)). The geometric method has the distinct advantage that it gives an experimental way to monitor the process of optimizing entanglement production.Comment: 8 pages, 1 figure

The impact of board and hotel characteristics on biodiversity reporting: Market diversification as a moderator

Purpose: This study aims to explain how board and hotel characteristics affect biodiversity reporting and to test the moderating effect of market diversification. Design/methodology/approach: The annual reports of 105 hotels were examined for the period between 2016 and 2017 to analyse these hotels’ biodiversity reporting using content analysis. The partial least squares technique was used to test the proposed relationships. Findings: The results show that the number of board members who are also on the corporate social responsibility committee, number of board members who are in environmental organizations, the star rating of the hotel, hotel size and hotel location have significant positive effects on the extent of biodiversity reporting. In addition, market diversification moderates positively the effects of number of board members with environmental experience and number of board members from environmental organizations on the extent of biodiversity reporting. Practical implications: The results of this study will be useful in enabling hotel manager and investors to become knowledgeable about these aspects of boards, which lead to higher biodiversity reporting. This study can also inform policymakers about the types of hotels that are less likely to disclose biodiversity reports and to develop effective enforcement of regulations. Originality/value: These findings extend the literature on biodiversity reporting by exploring the importance of board and hotel characteristics on the extent of biodiversity reporting and testing the moderating effect of market diversification

Anti-Candida activity of ethanolic extracts of Iranian endemic medicinal herbs against Candida albicans

It has long been known that herbs and their extracts have antimicrobial activities. Heracleum lasiopetalum Boiss., Satureja bachtiarica Bunge., Thymus daenensis Celak., Echiophora platyloba L., Dracocephalum multicaule Benth., Kelussia odoratissima Mozaff. and Achillea kellalensis Boiss. are Iranian endemic plant species that have been traditionally used as medicinal herbs and spices in different regions of Iran especially Central Zagross. Seven ethanolic extracts of endemic medicinal herbs and one extract of native medicinal herb (Stachys lavandulifolia Vahl.) collected from Chaharmahal va Bakhtiari province of Iran were assayed for the in vitro antifungal activity against Candida albicans (ATCC1023), using agar dilution methods. Most of the extracts showed relatively high anti-Candida activity against the tested fungi with the diameter of inhibition zone ranging between 8 and 17 mm. The extracts of S. bachtiarica and T. daenensis exhibited high inhibitory effect against C. albicans. The extracts of S. bachtiarica and T. daenensis were characterized using HPLC, the major components of S. bachtiarica and T. daenensis were carvacrol and thymol, respectively. The minimum inhibitory concentration (MIC) values for active extract range between 25 and 50 µg/ml. In conclusion, it can be said that the extract of some of the Iranian endemic medicinal plants (S. bachtiarica and T. daenensis) could be used as natural anti-Candida