Fusions of Palm Print with Palm-Phalanges Print and Palm Geometry

Purohit, Himanshu; Ajmera, Pawan K.

doi:10.1007/978-981-13-2673-8_59

Cited by 2 publications

(1 citation statement)

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“…Activity detection and monitoring systems have data from multiple modalities, and fusion techniques help to merge all modalities. In [32], [33], [34], and [35], they presented a survey on activity recognition leveraging fusion techniques. [36] and [37] presented a survey on biometric systems where fusion techniques were applied to merge multiple data.…”

Section: Related Studiesmentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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