Ms. Qinglan Wei Profile

Qinglan Wei

at Beijing Normal Univ

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Publications (2)

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SPIE Journal Paper | 11 April 2019

Spontaneous smile intensity estimation by fusing saliency maps and convolutional neural networks

Qinglan Wei, Elif Bozkurt, Louis-Philippe Morency, Bo Sun

JEI, Vol. 28, Issue 02, 023031, (April 2019) https://doi.org/10.1117/12.10.1117/1.JEI.28.2.023031

KEYWORDS: Databases, Samarium, Video, Lawrencium, Data modeling, Feature extraction, Convolutional neural networks, Visualization, Binary data, Mouth

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Smile intensity estimation plays important roles in applications such as affective disorder prediction, life satisfaction prediction, camera technique improvement, etc. In recent studies, many researchers applied only traditional features, such as local binary pattern and local phase quantization (LPQ) to represent smile intensity. To improve the performance of spontaneous smile intensity estimation, we introduce a feature set that combines the saliency map (SM)-based handcrafted feature and non-low-level convolutional neural network (CNN) features. We took advantage of the opponent-color characteristic of SMs and the multiple convolutional level features, which were assumed to be mutually complementary. Experiments were made on the Binghamton-Pittsburgh 4D (BP4D) database and Denver Intensity of Spontaneous Facial Action (DISFA) database. We set the local binary patterns on three orthogonal planes (LBPTOP) method as a baseline, and the experimental results show that the CNN features can better estimate smile intensity. Finally, through the proposed SM-LBPTOP feature fusion with the median- and high-level CNN features, we obtained the best result (52.08% on BP4D, 70.55% on DISFA), demonstrating our hypothesis is reasonable: the SM-based handcrafted feature is a good supplement to CNNs in spontaneous smile intensity estimation.

Proceedings Article | 14 September 2016 Paper

BNU-LSVED: a multimodal spontaneous expression database in educational environment

Bo Sun, Qinglan Wei, Jun He, Lejun Yu, Xiaoming Zhu

Proceedings Volume 9970, 997016 (2016) https://doi.org/10.1117/12.2235892

KEYWORDS: Databases, Video, Cameras, Psychology, Light sources and illumination, Eye, Head, Image segmentation, Reliability, Gold

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In the field of pedagogy or educational psychology, emotions are treated as very important factors, which are closely associated with cognitive processes. Hence, it is meaningful for teachers to analyze students’ emotions in classrooms, thus adjusting their teaching activities and improving students ’ individual development. To provide a benchmark for different expression recognition algorithms, a large collection of training and test data in classroom environment has become an acute problem that needs to be resolved. In this paper, we present a multimodal spontaneous database in real learning environment. To collect the data, students watched seven kinds of teaching videos and were simultaneously filmed by a camera. Trained coders made one of the five learning expression labels for each image sequence extracted from the captured videos. This subset consists of 554 multimodal spontaneous expression image sequences (22,160 frames) recorded in real classrooms. There are four main advantages in this database. 1) Due to recorded in the real classroom environment, viewer’s distance from the camera and the lighting of the database varies considerably between image sequences. 2) All the data presented are natural spontaneous responses to teaching videos. 3) The multimodal database also contains nonverbal behavior including eye movement, head posture and gestures to infer a student ’ s affective state during the courses. 4) In the video sequences, there are different kinds of temporal activation patterns. In addition, we have demonstrated the labels for the image sequences are in high reliability through Cronbach's alpha method.

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