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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2014)

Facial expression plays a significant role in human communication. It is considered the single most important cue in the psychology of emotion. Facial expression is taken as a universally understood signal, which triggers a discrete categorical basic emotion, including joy, sadness, fear, surprise, anger, and disgust. Thus, automatic analysis of emotion from images of human facial expression has been an interesting and challenging problem for the past 30 years. Aiming towards the applications of human behavior analysis, human-human interaction and human-computer interaction, this topic has recently drawn even more attention.

Automatic analysis of facial expression in a realistic scenario is a much more difficult problem due to that the 2-D imagery of human facial expression consists of rigid head motion and non-rigid muscle motion. We are tasked to solve this "coupled-motion" problem and analyze facial expression in a meaningful manner. We first proposed an image-based representation, Emotion Avatar Image, to help person-independent expression recognition. Second, an real-time registration technique is designed to improve frame-based streaming action unit (AU) recognition. The proposed accurate expression recognition techniques are then applied to the field of advertising, where audiences' commercial watching behavior is thoroughly analyzed.

    Cover page: What Can Spontaneous Facial Expression Tell Us?
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2015)
    Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods.
      Cover page: Person Re-Identification by Robust Canonical Correlation Analysis
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2015)
      Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration.
        Cover page: Efficient smile detection by Extreme Learning Machine
        • Article
        • Peer Reviewed
        UC Riverside Previously Published Works (2014)
        In marketing and advertising research, 'zapping' is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.
          Cover page: Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood
          • Article
          • Peer Reviewed
          UC Riverside Previously Published Works (2016)
            Cover page: Sparse representation matching for person re-identification
            • Article
            • Peer Reviewed
            UC Riverside Previously Published Works (2016)
              • Article
              • Peer Reviewed
              UC Riverside Previously Published Works (2017)
                Cover page: A dense flow-based framework for real-time object registration under compound motion
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