Taleb Alashkar, Co-Founder & CTOIn the beauty and cosmetics industry, a customer’s experience is where the sale is won or lost, where a brand is cherished, and where the seeds of loyalty are planted. Today, even after strategizing and implementing several customer-centric approaches, beauty brands are struggling to provide buyers with different avenues to evaluate makeup products and other cosmetics before purchase. Unavailability of makeup try on samples is one of the most common complaints. And even if they are available, consumers are doubtful about its hygiene as the same sample would be tested by multiple buyers. On the other hand, even consumers that choose to shop cosmetics online do not have a means of finding out if a color/shade of lipstick or foundation will match their complexion. Enter AlgoFace, a firm that aims to solve this long-standing challenge for makeup brands and cosmetics retailers by using leading-edge AR technology. The Michigan-based artificial intelligence startup fuses deep learning, machine vision, and augmented reality to create highly immersive virtual makeup try on solutions, thereby revolutionizing a makeup shopper’s POS experience.
AlgoFace empowers the cosmetics industry to hyper-personalize the way customers shop for beauty products. The company helps cosmetics brands build an AR makeup try on that can track and analyze a human face accurately and virtually show how a makeup product will look on them. “We offer this technology to our clients through SDK, so that it can be easily implemented on various web and mobile platforms,” explains Taleb Alashkar, co-founder and CTO of AlgoFace.
At the core of effectuating this pseudo-realistic makeup try on solution are the company’s proprietary Facial Landmark Tracking and Face Attribute Detection SDKs. The Facial Landmark Tracking can accurately detect 209 features points of a user’s face, paying attention to the minutest details like the corners of the eyes, the arch of the eyebrows, the tip of the nose, and more.
The Facial Landmark Tracking can accurately detect 209 features of a user’s face, paying attention to the minutest details
Complementary to that is the Face Attribute Detection SDK that looks at the user’s face and provides a detailed classification for 20 different facial traits such as skin tone, age, gender, lip shape, etc.
“Our AR-based makeup try on solutions can also double as a virtual shopping assistant for consumers with the help of its in-built recommender system,” says Alashkar. Based on a shopper’s facial characteristics, age, sex, ethnicity, and purchase history, AlgoFace’s AI-based recommender system suggests consumers appropriate cosmetic products. AlgoFace also offers a Makeup Look Transfer SDK that allows users to try on different celebrity or professional looks and get product recommendations for creating a similar effect. “Together, these SDKs help clients orchestrate a holistic solution that would help them stand out in a crowded and competitive cosmetics market,” informs Alashkar.
Today, making use of AI-driven AR solutions, AlgoFace has established an admirable footprint in the beauty and cosmetics landscape. Going forward, the company plans to develop additional solutions that would complement their existing SDKs. One such offering that AlgoFace is currently working on is a technology that will analyze a user’s skin condition and recommend suitable skin care products. Another AR solution that AlgoFace intends to add to their existing product suite is virtual hair color try on. “Above all, we are committed to developing AR products that will enhance the shopping experience of customers from all demographics,” concludes Alashkar.