Deep Learning Approach for Cosmetic Product Detection and Classification

As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beauty,and style.Thus,the identification of such products by using image recognition technology may aid in the identification of current commercial trends.In this paper,we propose a two-stage deep-learning detection and classification method for cosmetic products.Specifically,variants of the YOLO network are used for detection,where the bounding box for each given input product is predicted and subsequently cropped for classification.We use four state-of-the-art classification networks,namely ResNet,InceptionResNetV2,DenseNet,and EfficientNet,and compare their performance.Furthermore,we employ dilated convolution in these networks to obtain better feature representations and improve performance.Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy.Moreover,the dilated networks marginally outperform the base models,or achieve similar performance in the worst case.We conclude that the proposed method can effectively detect and classify cosmetic products.

Determination of phenolic whitening agents in cosmetics by micellar electrokinetic capillary chromatography with amperometric detection

A sensitive method for simultaneous determination of six phenolic whitening agents, including arbutin, phenol, resorcinol, hydroquinone, kojic acid, and salicylic acid in cosmetics has been developed using micellar electrokinetic capillary chromatography with amperometric detection （MECC-AD）. Effects of several factors, such as the pH value and concentration of running buffer, potential applied to the working electrode, separation voltage, and injection time were investigated to obtain optimum conditions for separation and detection. With a 75 cm long fused-silica capillary tube, well-defined separation of six phenolic compounds was achieved in 10 mmol/L SDS/40 mmol/L H3BO3-Na2B407 running buffer （pH 9.0）. Good linear relationship was obtained for each analyte over three orders of magnitude with correlation coefficients （r2） between 0.9985 and 0.9994, and the detection limit （S/N = 3） ranged from 0.04 p^g/mL to 0.45 p^g/mL. The proposed method has been successfully applied for the determination of phenolic whitening agents in real cosmetic samples with satisfactory results, providing an alternative monitoring method for cosmetics safety regulation.