[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124805-en":3,"doc-seo-124805-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124805,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Face Recognition approach via Deep and Machine Learning","Face recognition is a biometric technology used for identifying and verifying individuals through facial features, with applications spanning security, surveillance, and user authentication. Conventional approaches often struggle with reliable feature extraction and classifier choice, leading to reduced recognition performance. The proposed model combines a deep wavelet scattering transform network for feature extraction with machine learning classifiers. A four-stage pipeline covers image acquisition, pre-processing, feature extraction, and classification using SoftMax and SVM on the MULB dataset, achieving 98.29% with SVM and 97.87% with SoftMax.","Face Recognition approach via Deep and Machine  \nLearning  \nOla N. Kadhim  \nDepartment of Computer Science, Faculty of Computer Science & Mathematics, University of  \nKufa, Najaf, Iraq  \nTechnical Institute of Al-Mussaib, Al-Furat Al-Awsat Technical University, Najaf, Iraq  \n[ola.najah@atu.edu.iq](ola.najah@atu.edu.iq)  \nMohammed Hasan Abdulameer  \nDepartment of Computer Science, Faculty of Education for Girls, University of Kufa, Najaf,  \nIraq  \n[mohammed.almayali@uokufa.edu.iq](mohammed.almayali@uokufa.edu.iq)  \nAbstract—Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM) . We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier.  \nKeywords—Wavelet Scattering Network, Face recognition, Biometric, deep  \nlearning.  \n1 Introduction  \nBiometric recognition technology relies on physiological or behavioral attributes to identify individuals, has found extensive application across diverse sectors of society. Face recognition technology has emerged as a groundbreaking advancement in the field of biometric identification and surveillance [1] . With its ability to accurately identify individuals through their distinct facial characteristics, this technology has garnered considerable interest and extensive implementation in diverse fields. From enhancing security measures at airports and organizations to improving user authentication systems on smartphones, face recognition has revolutionized the way we interact with  \ntechnology and ensure safety in different environments [2] . Face recognition, utilizing traditional techniques, has long been a fundamental approach in the field of computer vision and biometric identification. Before the advent of deep learning and advanced algorithms, traditional face recognition methods relied on the extraction of handcrafted features and the application of statistical classifiers. These techniques involved analyzing facial characteristics such as shape, texture, and spatial relationships to establish identity. Although traditional face recognition methods may not possess the same level of accuracy and robustness as deep learning approaches, they have laid the foundation for the development of modern face recognition systems [3] . With deep learning, face recognition has revolutionized the field of computer vision and biometric identification. With its ability to automatically extract and analyze intricate facial features, deep learning has meaningfully improved the accuracy and efficiency of face recognition systems. Through the utilization of deep neural networks, these systems these systems have the capacity to acquire knowledge and understand complex patterns, allowing for robust identification and verification of individuals [4] . The deep wavelet scattering transform, known for its ability to capture multi-scale and invariant representations, has emerged as a promising approac","cbCaigoqFPgK3LIw","https://ap.wps.com/l/cbCaigoqFPgK3LIw","pdf",626056,1,13,"English","en",105,"# Abstract\n# Introduction\n# Related Literature\n# Research Background\n# Methodology and Proposed Approach\n# Experimental Results\n# Conclusion and Future Work","[{\"question\":\"What problem does the paper address in face recognition performance?\",\"answer\":\"Traditional face recognition struggles with difficult feature extraction and classifier selection, which can reduce recognition accuracy. The paper targets these issues using a deep feature extraction pipeline plus machine learning classification.\"},{\"question\":\"How does the proposed system extract facial features?\",\"answer\":\"It uses a deep wavelet scattering transform network to produce multi-scale, invariant representations by decomposing facial data across frequency bands and orientations.\"},{\"question\":\"Which classifier performs better in the experiments on the MULB dataset?\",\"answer\":\"The experiments show that SVM yields better results than SoftMax. The reported recognition accuracy is 98.29% with SVM versus 97.87% with SoftMax.\"}]","Face Recognition approach via Deep and Machine Learning | PDF",1785894750,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"face-recognition-approach-via-deep-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/face-recognition-approach-via-deep-and-machine-learning/124805/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in face recognition performance?","Question",{"text":75,"@type":76},"Traditional face recognition struggles with difficult feature extraction and classifier selection, which can reduce recognition accuracy. The paper targets these issues using a deep feature extraction pipeline plus machine learning classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system extract facial features?",{"text":80,"@type":76},"It uses a deep wavelet scattering transform network to produce multi-scale, invariant representations by decomposing facial data across frequency bands and orientations.",{"name":82,"@type":73,"acceptedAnswer":83},"Which classifier performs better in the experiments on the MULB dataset?",{"text":84,"@type":76},"The experiments show that SVM yields better results than SoftMax. The reported recognition accuracy is 98.29% with SVM versus 97.87% with SoftMax.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]