5 research outputs found
Objective Classes for Micro-Facial Expression Recognition
Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during machine learning training. We will show that classifying expressions
using Action Units, instead of predicted emotion, removes the potential bias of
human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D
feature descriptors. The experiments are evaluated on two benchmark FACS coded
datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when
classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the
result of the state-of-the-art 5-class emotional-based classification in CASME
II. Results indicate that classification based on Action Units provides an
objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for
journal revie
Less is More: Micro-expression Recognition from Video using Apex Frame
Despite recent interest and advances in facial micro-expression research,
there is still plenty room for improvement in terms of micro-expression
recognition. Conventional feature extraction approaches for micro-expression
video consider either the whole video sequence or a part of it, for
representation. However, with the high-speed video capture of micro-expressions
(100-200 fps), are all frames necessary to provide a sufficiently meaningful
representation? Is the luxury of data a bane to accurate recognition? A novel
proposition is presented in this paper, whereby we utilize only two images per
video: the apex frame and the onset frame. The apex frame of a video contains
the highest intensity of expression changes among all frames, while the onset
is the perfect choice of a reference frame with neutral expression. A new
feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to
encode essential expressiveness of the apex frame. We evaluated the proposed
method on five micro-expression databases: CAS(ME), CASME II, SMIC-HS,
SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with
our proposed technique achieving a state-of-the-art F1-score recognition
performance of 61% and 62% in the high frame rate CASME II and SMIC-HS
databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment
of grant support adde
Sparse tensor canonical correlation analysis for micro-expression recognition
A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) [1] code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information. (C) 2016 Elsevier B.V. All rights reserved
Automatic inference of latent emotion from spontaneous facial micro-expressions
Emotional states exert a profound influence on individuals' overall well-being, impacting them both physically and psychologically. Accurate recognition and comprehension of human emotions represent a crucial area of scientific exploration. Facial expressions, vocal cues, body language, and physiological responses provide valuable insights into an individual's emotional state, with facial expressions being universally recognised as dependable indicators of emotions. This thesis centres around three vital research aspects concerning the automated inference of latent emotions from spontaneous facial micro-expressions, seeking to enhance and refine our understanding of this complex domain.
Firstly, the research aims to detect and analyse activated Action Units (AUs) during the occurrence of micro-expressions. AUs correspond to facial muscle movements. Although previous studies have established links between AUs and conventional facial expressions, no such connections have been explored for micro-expressions. Therefore, this thesis develops computer vision techniques to automatically detect activated AUs in micro-expressions, bridging a gap in existing studies.
Secondly, the study explores the evolution of micro-expression recognition techniques, ranging from early handcrafted feature-based approaches to modern deep-learning methods. These approaches have significantly contributed to the field of automatic emotion recognition. However, existing methods primarily focus on capturing local spatial relationships, neglecting global relationships between different facial regions. To address this limitation, a novel third-generation architecture is proposed. This architecture can concurrently capture both short and long-range spatiotemporal relationships in micro-expression data, aiming to enhance the accuracy of automatic emotion recognition and improve our understanding of micro-expressions.
Lastly, the thesis investigates the integration of multimodal signals to enhance emotion recognition accuracy. Depth information complements conventional RGB data by providing enhanced spatial features for analysis, while the integration of physiological signals with facial micro-expressions improves emotion discrimination. By incorporating multimodal data, the objective is to enhance machines' understanding of latent emotions and improve latent emotion recognition accuracy in spontaneous micro-expression analysis