Face and Image Recognition is not only about security and surveillance or control over 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.
Although Face and Image recognition is an AI frontrunner as far as security, agriculture, and industrial QA, are concerned, 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 obtaining better capabilities to raise their sights and reach higher quality and profitability levels.
Meanwhile, the Image Recognition- inspired and – enabled opportunities, that have been cropping up of late 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, that have 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 think otherwise. More so because AI and Image Recognition, in particular, is already shaping some of the novel ways in which most of the businesses in these industries will operate in the future.
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 that have 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 not so distant 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. These technologies can make the process a relatively quick and simple procedure that would require 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 quality.
Subsequently, a customer could simply pick a model from an online catalog and submit their recent enough measurements for the 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, making a piece of clothing a much better, if not 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 attention.
However, the use of Image Recognition and Computer Vision bodes a lot more boon for larger-scale apparel manufacturers and merchants than a 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. These devices 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, the customer 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. Besides, Image Recognition allows using a snapshot of a garment to find similar clothing items in an online catalog. 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 enabling the user to try 1000 foundations alone, the app allows them to apply a multitude of foundation, concealer, and lip shade combinations — something infeasible to do otherwise.
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 completely revolutionized by the app, becoming a captivating pastime that is a lot more likely to result in a good find?
The successful pioneering move by Sephora is a great 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 provide 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.