A5023346445- Face and Expression Recognition
- Face recognition and analysis
- Emotion and Mood Recognition
- Speech and Audio Processing
- Domain Adaptation and Few-Shot Learning
- Forecasting Techniques and Applications
- Data-Driven Disease Surveillance
- Video Surveillance and Tracking Methods
- Financial Risk and Volatility Modeling
- Identification and Quantification in Food
- Stock Market Forecasting Methods
- Anomaly Detection Techniques and Applications
- Food Supply Chain Traceability
- Machine Learning and ELM
- Music and Audio Processing
- Imbalanced Data Classification Techniques
- Human Pose and Action Recognition
- Explainable Artificial Intelligence (XAI)
- Generative Adversarial Networks and Image Synthesis
- Face Recognition and Perception
Boğaziçi University
2015-2018
We propose a two-level system for apparent age estimation from facial images. Our first classifies samples into overlapping groups. Within each group, the is estimated with local regressors, whose outputs are then fused final estimate. use deformable parts model based face detector, and features pretrained deep convolutional network. Kernel extreme learning machines used classification. evaluate our on ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, report 0.3740...
This paper presents our contribution to ACM ICMI 2015 Emotion Recognition in the Wild Challenge (EmotiW 2015). We participate both static facial expression (SFEW) and audio-visual emotion recognition challenges. In challenges, we use a set of visual descriptors their early late fusion schemes. For AFEW, also exploit popularly used spatio-temporal modeling alternatives carry out multi-modal fusion. classification, employ two least squares regression based learners that are shown be fast...
We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed input CV, using rich feature sets. These multiple modalities fed into modality-specific regressors to predict apparent personality traits a variable that predicts whether the subject will be invited interview. The base learners stacked ensemble of decision trees produce outputs quantitative stage, single tree, combined with...
Affective computing, particularly emotion and personality trait recognition, is of increasing interest in many research disciplines. The interplay shows itself the first impression left on other people. Moreover, ambient information, e.g. environment objects surrounding subject, also affect these impressions. In this work, we employ pre-trained Deep Convolutional Neural Networks to extract facial information from images for predicting apparent personality. We investigate Local Gabor Binary...
Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they in certain tasks related to human behavior analysis such as health care applications. Despite their importance, it is only recently that researchers starting explore these aspects. This paper provides an introduction explainability the context vision with emphasis on looking at people tasks. Specifically, we review study those mechanisms first impressions analysis. To best...
A new approach is provided in our paper for creating a strategic early warning system allowing the estimation of future state milk market as scenarios. This line with recent call from EU commission tools that help to better address such highly volatile market. We applied different multivariate time series regression and Bayesian networks on pre-determined map relations between macro-economic indicators. The evaluation findings root mean square error (RMSE) performance score enhances...