Machine learning method for cosmetic product recognition: a visual searching approach

Saiyed Umer, Partha Pratim Mohanta, Ranjeet Kumar Rout, Hari Mohan Pandey*

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

14 Citations (Scopus)
73 Downloads (Pure)


A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

Original languageEnglish
Article numberMTAP-D-19-04018R1
JournalMultimedia Tools and Applications
Early online date12 Jun 2020
Publication statusE-pub ahead of print - 12 Jun 2020


  • Cosmetic products
  • E-commerce application
  • Feature extraction
  • Machine learning
  • Visual search


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