Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition
- Resource Type
- Periodical
- Authors
- Goyal, A.; Meenpal, T.
- Source
- IEEE Transactions on Image Processing IEEE Trans. on Image Process. Image Processing, IEEE Transactions on. 30:191-206 2021
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Computing and Processing
Feature extraction
Face recognition
Image recognition
Wavelet transforms
Deep learning
Measurement
Kinship recognition
feature extraction
dual-tree complex wavelet transform
facial patches
global representation
local representation
patch selection
selective representation
- Language
- ISSN
- 1057-7149
1941-0042
Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT): Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).