20 research outputs found
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Facial expression data is characterized by a significant imbalance, with most
collected data showing happy or neutral expressions and fewer instances of fear
or disgust. This imbalance poses challenges to facial expression recognition
(FER) models, hindering their ability to fully understand various human
emotional states. Existing FER methods typically report overall accuracy on
highly imbalanced test sets but exhibit low performance in terms of the mean
accuracy across all expression classes. In this paper, our aim is to address
the imbalanced FER problem. Existing methods primarily focus on learning
knowledge of minor classes solely from minor-class samples. However, we propose
a novel approach to extract extra knowledge related to the minor classes from
both major and minor class samples. Our motivation stems from the belief that
FER resembles a distribution learning task, wherein a sample may contain
information about multiple classes. For instance, a sample from the major class
surprise might also contain useful features of the minor class fear. Inspired
by that, we propose a novel method that leverages re-balanced attention maps to
regularize the model, enabling it to extract transformation invariant
information about the minor classes from all training samples. Additionally, we
introduce re-balanced smooth labels to regulate the cross-entropy loss, guiding
the model to pay more attention to the minor classes by utilizing the extra
information regarding the label distribution of the imbalanced training data.
Extensive experiments on different datasets and backbones show that the two
proposed modules work together to regularize the model and achieve
state-of-the-art performance under the imbalanced FER task. Code is available
at https://github.com/zyh-uaiaaaa.Comment: Accepted by NeurIPS202
Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is
essential for establishing the beginning of treatment, enriching the outlook,
and enhancing the overall well-being of those affected individuals. However,
early diagnosis and detecting the disease's signs is not straightforward. A
simpler and cheaper way arises by analyzing the patient's facial expressions
through computational methods. When a patient with ALS engages in specific
actions, e.g., opening their mouth, the movement of specific facial muscles
differs from that observed in a healthy individual. This paper proposes Facial
Point Graphs to learn information from the geometry of facial images to
identify ALS automatically. The experimental outcomes in the Toronto Neuroface
dataset show the proposed approach outperformed state-of-the-art results,
fostering promising developments in the area.Comment: 7 pages and 7 figure
EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours are represented by strings. However, starting point invariance is difficult to achieve. One interesting possibility is the use of cyclic strings, which are strings with no starting and final points. Here we present the use of Hidden Markov Models for modelling cyclic strings and their training using Expectation Maximization. Experimental results show that our proposal outperforms other methods in the literature