Cosmetics Logos Feature Detection: A Deep Learning Approach Using VAE

Logos are an essential part of brand identity because they capture strategic, cultural, and aesthetic components that appeal to customers. This research uses a deep learning-based feature extraction algorithm to examine the visual and design trends of the logos of the top 850 cosmetics brands; additionally, the viewers’ perceptions towards an extracted logo were quantitatively examined. Using the Variational autoencoder (VAE), this study finds and classifies the design components of these logos, including color, format and shape, font, complexity, symmetry, and orientation. The findings indicate important patterns, including a predilection for simple designs, the deliberate use of monochromatic as opposed to vivid color schemes, and the popularity of serif and sans-serif typefaces for conveying sophistication and modernity, respectively. These observations demonstrate how consumer psychology and visual design interact in the cosmetics sector. This study not only advances knowledge of branding strategies in the beauty industry but also demonstrates the deep learning potentiality to glean useful insights from visual data. The results can be guidelines for future logo designs and brands to strengthen their identity in a competitive market.