The application of chitosan (CS) and whey protein (WP) alone or in combination in 3D/4D printing has been well considered in previous studies. Although several excellent reviews on additive manufacturing discussed the properties and biomedical applications of CS and WP, there is a lack of a systemic review about CS and WP bio-inks for 3D/4D printing applications. Easily modified bio-ink with optimal printability is a key for additive manufacturing. CS, WP, and WP–CS complex hydrogel possess great potential in making bio-ink that can be broadly used for future 3D/4D printing, because CS is a functional polysaccharide with good biodegradability, biocompatibility, non-immunogenicity, and non-carcinogenicity, while CS–WP complex hydrogel has better printability and drug-delivery effectivity than WP hydrogel. The review summarizes the current advances of bio-ink preparation employing CS and/or WP to satisfy the requirements of 3D/4D printing and post-treatment of materials. The applications of CS/WP bio-ink mainly focus on 3D food printing with a few applications in cosmetics. The review also highlights the trends of CS/WP bio-inks as potential candidates in 4D printing. Some promising strategies for developing novel bio-inks based on CS and/or WP are introduced, aiming to provide new insights into the value-added development and commercial CS and WP utilization.

Fingerprint recognition is the most widely used identification method at present. However, it still falls short in terms of cross-platform and algorithmic complexity, which exerts a certain effect on the migration of fingerprint data and the development of the system. The conventional image recognition methods require offline standard databases constructed in advance for image access efficiency. The database can provide a pre-processed image via a specific method that probably is compatible merely with the specific recognition algorithm. Then, the specific recognition algorithm starts the process of retrieving these specific pre-proessing images for recognition and inevitably will be blocked from other datasets. The proposed method in this research designed an embedded image processing algorithm based on a Siamese neural network in the recognition method that allows the proposed method to recognize images from any source without constructing a database for image storage in advance. In this research, the proposed method was applied to fingerprint recognition and evaluation of the proposed method was evaluated. The results showed that the accuracy of the proposed algorithm was up to 92%, and itsF1 score was up to 0.87. Compared with the conventional fingerprint matching methods, its significant advantage in the FRR, FAR, and CR jointly indicated the remarkable correct recognition rate of the proposed method.