Authors : Katroth Balakrishna Maruthiram; Ranga Muralikrishna

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/yt6bep5j

Scribd : https://tinyurl.com/27pz8vmj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG1184

Abstract : Face Morphing is a technique that involves blending two or more faces to create new often realistic- looking images. These morphed images are generated or created using morphing techniques or photo manipulation tools pose a significant peril to face recognition systems. In this paper, we proposed a method to improve deep learning based face morphing detection systems to be more robust against face morphing attacks. Leveraging deep learning models and algorithms including MTCNN (Multitask Cascaded Convolutional Neural Network) for efficient face extraction from original and morphed faces with a high accuracy and FaceNet for extracting unified embeddings from the faces. The project aims to push the boundaries of face morphing detection capabilities by leveraging feature combination techniques using cosine distance and SSIM(structural similarity index measure) for identifying the similarity between faces and applying spatial attention mechanism which aims to enhance the feature representation learned by the model by focusing on the most informative parts of the image and training support vector machine classifier and voting classifier using the extracted embeddings significantly helps in building a robust face morphing detection system.

Keywords : MTCNN, Feature Combination, FaceNet, Attention Mechanism, SSIM, Cosine Distance.

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