Cross-Centroid Ripple Pattern for Facial Expression Recognition
- URL: http://arxiv.org/abs/2201.05958v1
- Date: Sun, 16 Jan 2022 03:32:58 GMT
- Title: Cross-Centroid Ripple Pattern for Facial Expression Recognition
- Authors: Monu Verma, Prafulla Saxena, Santosh Kumar Vipparthi, Girdhari Singh
- Abstract summary: We propose a new feature descriptor Cross-Centroid Ripple Pattern (CRIP) for facial expression recognition.
CRIP encodes the transitional pattern of a facial expression by incorporating cross-centroid relationship between two ripples located at radius r1 and r2 respectively.
- Score: 7.218497970427467
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we propose a new feature descriptor Cross-Centroid Ripple
Pattern (CRIP) for facial expression recognition. CRIP encodes the transitional
pattern of a facial expression by incorporating cross-centroid relationship
between two ripples located at radius r1 and r2 respectively. These ripples are
generated by dividing the local neighborhood region into subregions. Thus, CRIP
has ability to preserve macro and micro structural variations in an extensive
region, which enables it to deal with side views and spontaneous expressions.
Furthermore, gradient information between cross centroid ripples provides
strenght to captures prominent edge features in active patches: eyes, nose and
mouth, that define the disparities between different facial expressions. Cross
centroid information also provides robustness to irregular illumination.
Moreover, CRIP utilizes the averaging behavior of pixels at subregions that
yields robustness to deal with noisy conditions. The performance of proposed
descriptor is evaluated on seven comprehensive expression datasets consisting
of challenging conditions such as age, pose, ethnicity and illumination
variations. The experimental results show that our descriptor consistently
achieved better accuracy rate as compared to existing state-of-art approaches.
Related papers
- SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting [11.978551396144532]
In this paper, we propose an efficient framework for facial expression spotting.
First, we propose a Sliding Window-based Multi-Resolution Optical flow (SW-MRO) feature, which calculates multi-resolution optical flow of the input sequence within compact sliding windows.
Second, we propose SpotFormer, a multi-scale-temporal Transformer that simultaneously encodes facial-temporal relationships of the SW-MRO features for accurate frame-level probability estimation.
Third, we introduce supervised contrastive learning into SpotFormer to enhance the discriminability between different types of expressions.
arXiv Detail & Related papers (2024-07-30T13:02:08Z) - Text-Guided Face Recognition using Multi-Granularity Cross-Modal
Contrastive Learning [0.0]
We introduce text-guided face recognition (TGFR) to analyze the impact of integrating facial attributes in the form of natural language descriptions.
TGFR demonstrates remarkable improvements, particularly on low-quality images, over existing face recognition models.
arXiv Detail & Related papers (2023-12-14T22:04:22Z) - Exploring Invariant Representation for Visible-Infrared Person
Re-Identification [77.06940947765406]
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy.
In this paper, we address the problem from both image-level and feature-level in an end-to-end hybrid learning framework named robust feature mining network (RFM)
Experiment results on two standard cross-spectral person re-identification datasets, RegDB and SYSU-MM01, have demonstrated state-of-the-art performance.
arXiv Detail & Related papers (2023-02-02T05:24:50Z) - Local Gradient Hexa Pattern: A Descriptor for Face Recognition and
Retrieval [20.77994516381]
A local gradient hexa pattern (LGHP) is proposed that identifies the relationship amongst the reference pixel and its neighboring pixels.
Discriminative information exists in the local neighborhood as well as in different derivative directions.
The proposed descriptor has better recognition as well as retrieval rates compared to state-of-the-art descriptors.
arXiv Detail & Related papers (2022-01-03T07:45:36Z) - MC-LCR: Multi-modal contrastive classification by locally correlated
representations for effective face forgery detection [11.124150983521158]
We propose a novel framework named Multi-modal Contrastive Classification by Locally Correlated Representations.
Our MC-LCR aims to amplify implicit local discrepancies between authentic and forged faces from both spatial and frequency domains.
We achieve state-of-the-art performance and demonstrate the robustness and generalization of our method.
arXiv Detail & Related papers (2021-10-07T09:24:12Z) - Learning Fair Face Representation With Progressive Cross Transformer [79.73754444296213]
We propose a progressive cross transformer (PCT) method for fair face recognition.
We show that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance.
arXiv Detail & Related papers (2021-08-11T01:31:14Z) - Dual-Cross Central Difference Network for Face Anti-Spoofing [54.81222020394219]
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
Central difference convolution (CDC) has shown its excellent representation capacity for the FAS task.
We propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features.
arXiv Detail & Related papers (2021-05-04T05:11:47Z) - Hierarchical Deep CNN Feature Set-Based Representation Learning for
Robust Cross-Resolution Face Recognition [59.29808528182607]
Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics.
Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space.
In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
arXiv Detail & Related papers (2021-03-25T14:03:42Z) - Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
We propose a unified framework calledInter-class DiscrepancyAlignment(IDA)
IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors.
IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
arXiv Detail & Related papers (2021-03-02T08:20:08Z) - Robust Facial Landmark Detection by Cross-order Cross-semantic Deep
Network [58.843211405385205]
We propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection.
Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning.
A novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.
arXiv Detail & Related papers (2020-11-16T08:19:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.