{"id":290,"date":"2026-02-18T10:41:43","date_gmt":"2026-02-18T15:41:43","guid":{"rendered":"https:\/\/carleton.ca\/share\/?p=290"},"modified":"2026-02-18T10:41:43","modified_gmt":"2026-02-18T15:41:43","slug":"new-publication-2","status":"publish","type":"post","link":"https:\/\/carleton.ca\/share\/2026\/new-publication-2\/","title":{"rendered":"New Publication"},"content":{"rendered":"

Hejazi, Elhamsadat, Majid Ahmadi, and Arash Ahmadi. “Robust Face Recognition and Classification Under Occlusion Using a Refined Transformer-Based Attention Mechanism.”\u00a0IEEE Canadian Journal of Electrical and Computer Engineering<\/i>\u00a0(2026)<\/a>.<\/p>\n

Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model\u2014channel-spatial faster vision transformer (CSFVIT)\u2014that integrates local and global feature processing to enhance recognition performance under diverse occlusion scenarios. The local branch refines facial features using a parallel channel-spatial attention (PCSA) module based on ResNet-18, while the global branch leverages a faster vision Transformer (FasterViT) to capture long-range dependencies. A dynamic attention fusion (DAF) module adaptively balances these features based on occlusion severity. We validate our model on five benchmark datasets: CASIA-WebFace, LFW, Extended Yale B, ORL, and AR. The model achieves 97.46% accuracy on CASIA-WebFace, 97.62% on LFW, 99.39% on Extended Yale B, 98.78% on ORL, and 98.50% on AR (sunglasses)\/97.50% (scarf), consistently outperforming state-of-the-art baselines. CSFVIT achieves consistently high recognition accuracy under both synthetic and real-world occlusions, outperforming several attention- and transformer-based baselines. This practical and efficient architecture demonstrates strong potential for real-world face recognition applications in unconstrained environments.<\/p>\n","protected":false},"excerpt":{"rendered":"

Hejazi, Elhamsadat, Majid Ahmadi, and Arash Ahmadi. “Robust Face Recognition and Classification Under Occlusion Using a Refined Transformer-Based Attention Mechanism.”\u00a0IEEE Canadian Journal of Electrical and Computer Engineering\u00a0(2026). Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model\u2014channel-spatial faster vision transformer […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"yoast_head":"\nNew Publication - Specific-domain Hardware Research Laboratory<\/title>\n<meta name=\"description\" content=\"Hejazi, Elhamsadat, Majid Ahmadi, and Arash Ahmadi. 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