Face and Image Recognition is not only about security and surveillance or controlling the quality of industrial production processes. The technology is proving increasingly impactful to the fashion and beauty industries, generating multiple exciting opportunities for manufacturers and consumers alike.


Face and Image recognition being an AI frontrunner in terms of security, agriculture, and industrial QA, the technology’s business uses beyond these three realms are still much less known.

As a result, many businesses in industries other than security and surveillance, agriculture, and industrial production have barely given any thought to employing Image Recognition as a means of attaining better capabilities to raise their sights and achieve higher levels of quality and profitability.

Meanwhile, the Image Recognition- inspired and - enabled opportunities, which have been cropping up of late elsewhere, can barely be ignored and should be taken note of by a much, much wider audience. Who else can they prove impactful to before long?

Well, it wouldn’t be a leap of intuition to conjecture that the industries, in which Face and Image Recognition will be growing in importance during the next several decades (while also generating a multiplicity of profitable opportunities, of course) include those, having to do with people’s appearance. At least, with the world’s population currently sitting at around 7.7 billion (September, 2019) and predicted by the UN to reach some 9.7 billion by 2050, it wouldn’t be too shrewd to deem otherwise. More so because AI and Image Recognition, in particular, are already shaping some of the novel ways in which most of the businesses in these industries may start operating in the offing.

Just how much footing has Image Recognition already gained in the Fashion and Beauty space? How exactly is it used? Can any more uses of Image Recognition be discovered and made a reality in the said niches in the near future by those, looking for their piece of AI action?

Let’s try to answer, at least, some of these questions here.

The Apparel Industry

The rag trade has probably been around since mammoths roamed the earth, and the process of pelts being transformed into more comfortable and elegant wear has remained almost untouched for millennia. Well, with the exception, of, perhaps, various sewing machines having been introduced to automate part of this process. On the face of it, what could ever change in this pin-riddled, centuries-old business? The answer is “quite a bit,” and many tailoring pros of today may be given a run for their money in the future. How can that happen?

Firstly, ML-empowered Image Recognition and Computer Vision can demystify altogether the process of taking measurements and fitting tailor-made clothes. They can render this process a relatively quick and simple procedure, requiring very little skill, if any: a regular cubicle, equipped with several computer-connected cameras, can be used to take body measurements with amazing precision and unerringly.

Subsequently, a customer could simply pick a model from an online catalog and submit their recent enough measurements for an item to be manufactured to order. Moreover, Computer Vision & Image Recognition -equipped sewing machines (and these are already in existence) can be used to tailor textiles with greater precision based on such measurements, thereby making a piece of clothing a better, if perfect, fit.

Using the Image Recognition technology for this purpose could give the bespoke tailoring niche a huge boost, tipping the scales in favor of tailor-made garments in the case of a vast number of garment manufacturers. In essence, this alone is enough for Image Recognition to be worth their most close attention.

However, the use of Image Recognition and Computer Vision bodes a lot more boon for larger-scale apparel manufacturers and merchants than the mere chance to diversify their business and get hooked into larger-than-average bespoke tailoring. In addition to its better known QA applications (for instance, textile quality control and seam puckering-related QA), Image Recognition allows them to equip their brick-and-mortar and online stores with Computer Vision- and ML-connected devices that can help find well-fitting and becoming garments for a specific person based on the person’s looks, prevailing recent trends, and fashion designers’ recommendations.

An example of this kind of a device is Amazon’s Echo Look, intended to help a customer make a good choice from among a host of outfits by inquiring from Alexa. In a nutshell, one can use a voice-activated camera to take head-to-toe snapshots or short videos of themselves, ask Alexa for styling advice, receive recommendations on how one's present outfit can be complemented with other clothes, compare different outfits, view them from different angles, and, even, share the outfits they like with their friends.  

As far as online shopping is concerned, a visitor to a website can be offered the option of getting familiar with clothing items that are similar to the one that has taken their fancy. Image Recognition also allows using a snapshot of a garment to find similar clothing items in an online catalogue. In particular, the latter kind of a solution is presently offered by the French AI company Watiz. Incidentally, their Epick app allows the user to find clothes similar to those they have seen worn in TV shows.

The Beauty & Personal Care Industry

The Make-Up Niche

Worth, according to Forbes, $445 billion in the recent year of 2017, the Beauty industry is just another example of a vertical where Face and Image Recognition is bound to make a lot of positive impact.

By capturing the features of a person’s face and utilizing the Image Recognition and VR technology, a Face Recognition app can allow one to apply an infinite number of makeup combinations virtually. This is precisely what the Sephora Virtual Artist, launched in 2016 by the well-known cosmetics brand of the same name, is designed to do.

The app scans the user’s face, detecting the eye, cheek, and lip landmarks, and uses these as “placeholders” for the virtual application of the various possible makeup options. Thus, one can instantly try on lip, eye, and cheek makeup.

It is possible to arm-swatch hundreds of eyeshadow pallettes, which makes it easy to quickly compare the shades. While allowing one to try 1000 foundations alone, the app allows applying a multitude of foundation, concealer, and lip shade combinations, - something infeasible to do conventionally.

Furthermore, Sephora's app conveniently makes it possible to color-match one's makeup selection to the clothes they will be wearing, -no less than a magical wand to help ladies smarten themselves up comprehensively. Does it really have to be mentioned that the often frustrating and time-consuming quest for that only combination that sizes up to the occasion at hand is utterly revolutionized by the app, becoming a captivating pastime, a lot more likely to result in a good find? 

The successful pioneering move by Sephora is a buoyant example of how Face and Image Recognition can benefit both the manufacturers of cosmetics and their consumers.

The Skin and Hair Care Niche

Looking good is seldom separate from skin and hair care, and is nearly always dependent on it to some extent after a certain age. That makes the global skincare products’ market involve hundreds of millions of consumers, who, according to Statista, will have made it worth a mind-boggling 180.3 (!) billion United States dollars by 2024. No wonder, the skincare space is fast becoming a fertile ground for AI innovations and already has been explored and populated by several AI vendors -worth your attention.

Presently, there are several skin and hair care success stories that illustrate the usefulness and innovative potential of Image Recognition in the niche. The key distinction AI allows skin and hair care vendors to gain, is, seemingly, the same as in the rest of the industries under review, - the ability to create bespoke product combinations or, even, bespoke products, as such, for each particular customer. While the latter is achieved through the use of Machine Learning algorithms and hefty skincare product ingredient databases (take the example of Proven), the former can best be done by employing Image Recognition. For example, Procter & Gamble’s brand Olay utilizes Machine Learning image processing algorithms to analyze user-submitted selfies and come up with tailored product purchase recommendations.

With the overall usage stats being, according to VentureBeat, in the region of 1.2 million times and an impressive 5000-7000 users per day (as of early 2017), the brand’s skincare app’s success shows just another time that the skin care space and the adjacent niches seem to hold tremendous opportunities for Image Recognition providers. Indeed, in a number of uses and areas, from the choice of footwear and tattoo patterns, to that of jewelry to plastic surgery, using Face and Image Recognition and Machine Learning looks like the best way to generate new lucrative opportunities and advance the corresponding industry.

Disclaimer: The present article reflects solely the subjective viewpoint of the SYTOSS Team on the topics, covered herein, and does not represent a guarantee or endorsement of any kind or an advice to buy or not to buy, or use or not to use any software or other product, service, or technology.

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