Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)

TL;DR

This paper introduces CaVINet, a deep learning model for cross-modal caricature and visual face verification and recognition, achieving high accuracy without manual landmark annotations and handling extreme distortions.

Contribution

The paper presents the first deep cross-modal architecture for caricature verification that learns shared representations without relying on facial landmarks or identity labels.

Findings

Achieved 91% accuracy in verifying unseen images.

Achieved 75% accuracy in verifying unseen identities.

Attained 85% rank-1 accuracy for caricature recognition.

Abstract

Learning from different modalities is a challenging task. In this paper, we look at the challenging problem of cross modal face verification and recognition between caricature and visual image modalities. Caricature have exaggerations of facial features of a person. Due to the significant variations in the caricatures, building vision models for recognizing and verifying data from this modality is an extremely challenging task. Visual images with significantly lesser amount of distortions can act as a bridge for the analysis of caricature modality. We introduce a publicly available large Caricature-VIsual dataset [CaVI] with images from both the modalities that captures the rich variations in the caricature of an identity. This paper presents the first cross modal architecture that handles extreme distortions of caricatures using a deep learning network that learns similar representations across the modalities. We use two convolutional networks along with transformations that are subjected to orthogonality constraints to capture the shared and modality specific representations. In contrast to prior research, our approach neither depends on manually extracted facial landmarks for learning the representations, nor on the identities of the person for performing verification. The learned shared representation achieves 91% accuracy for verifying unseen images and 75% accuracy on unseen identities. Further, recognizing the identity in the image by knowledge transfer using a combination of shared and modality specific representations, resulted in an unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1 accuracy for visual images.