{"id":14486,"date":"2022-02-17T22:53:07","date_gmt":"2022-02-17T22:53:07","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/17\/heres-why-your-video-doorbell-sucks-at-recognizing-you-review-geek\/"},"modified":"2022-02-17T22:53:07","modified_gmt":"2022-02-17T22:53:07","slug":"heres-why-your-video-doorbell-sucks-at-recognizing-you-review-geek","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/17\/heres-why-your-video-doorbell-sucks-at-recognizing-you-review-geek\/","title":{"rendered":"Here\u2019s Why Your Video Doorbell Sucks at Recognizing You \u2013 Review Geek"},"content":{"rendered":"

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Ring<\/span><\/figcaption><\/figure>\n

Of all the features included in video doorbells, facial recognition might be the most underappreciated. There\u2019s nothing like hearing your smart speakers call out \u201cmom is at the door\u201d before she can even knock. Unfortunately, people have a good reason to dismiss this feature\u2014it doesn\u2019t work.<\/p>\n

Visit a smart brand\u2019s\u00a0support forum<\/a>, and you\u2019ll see a ton of people complaining that their smart doorbell misidentifies every guest. Video doorbells think that toddlers are grandfathers, or that every pizza boy is your spouse. And there isn\u2019t much you can do to solve this problem. Smart doorbells just suck at facial recognition.<\/p>\n

Doorbell Cameras Don\u2019t Capture Enough Detail<\/h2>\n
\"Three
Cameron Summerson<\/span><\/figcaption><\/figure>\n

The facial recognition technology utilized by smart doorbells is pretty cutting-edge, as it employs some of the most advanced AI ever created. But even with great AI, video doorbells don\u2019t have the hardware to really support facial recognition\u2014they just can\u2019t capture enough detail.<\/p>\n

Like most facial recognition systems<\/a>, smart doorbells capture and analyze 2D images. These flat images contain a lot of useful info, such as the width of your mouth, your skin tone, and the space between your eyes. But this data may not be unique to your face. In fact, this data may not be all that accurate, as video doorbells capture fairly low-res images of moving subjects.<\/p>\n

More advanced facial recognition systems, such as the one that\u2019s in your iPhone, capture \u201c3D\u201d images using infra-red TOF cameras<\/a>. Here\u2019s the gist; they shoot invisible lasers at your face and measure the time it takes each laser to bounce back. The data captured by these TOF cameras contributes to a \u201cdepth map,\u201d which contains measurements like the length of your nose or the angle of your ears.<\/p>\n

That 3D data is a lot more useful than what your video doorbell captures, for reasons that should be obvious.\u00a0But hardware isn\u2019t the only problem here. In the grand scheme of things, the advanced AI used by your video doorbell is actually pretty rudimentary.<\/p>\n

Facial Recognition Algorithms Need Training<\/h2>\n

\"The<\/p>\n

The facial recognition systems offered in smart doorbells are \u201cself-learning.\u201d They may ask you to identify a new person, but for the most part, they build and organize a facial database without user input. And that can present some problems<\/a>.<\/p>\n

See, self-learning facial recognition systems are always trying to improve their accuracy. That means collecting a ton of data; it\u2019s hard for your doorbell to identify someone if it\u2019s only seen that person once. So, every time your doorbell sees \u201cmom,\u201d it adds to its collection of \u201cthis is what mom looks like.\u201d<\/p>\n

But when a plumber comes to your door and gets incorrectly identified as \u201cmom,\u201d the facial recognition system becomes less accurate. Your doorbell doesn\u2019t know it made a mistake, and suddenly, guests with a mustache could be \u201cmom.\u201d This leads to a downward spiral\u2014the loss of accuracy creates more false positives, and \u201cmom\u201d now comes in every size, shape, and skin color. In the eyes of your doorbell, everyone is \u201cmom.\u201d<\/p>\n

It\u2019s like when a student learns a math equation incorrectly. They may be confident in what they learned, but until they bomb an exam, they won\u2019t realize that they screwed up. Students need someone to check that they\u2019re learning stuff correctly, and the same goes for AI.<\/p>\n

Unfortunately, you\u2019re the teacher in this situation.<\/p>\n

How to Improve Your Doorbell\u2019s Facial Recognition<\/h2>\n

\"The<\/p>\n

Improving your doorbell\u2019s facial recognition system is a chore. There\u2019s no permanent fix here\u2014you need to actively keep up with the facial recognition system to address its mistakes.<\/p>\n

First thing\u2019s first, you need to ensure that your smart doorbell can clearly see guests\u2019 faces. That may mean repositioning the doorbell, regularly cleaning its lens, or adding some lights<\/a> outside your front door.<\/p>\n

Once you know that your doorbell can see what it\u2019s supposed to see, you need to clean up its facial recognition database. This process will differ for each doorbell, but in most cases, you can find a list of faces in your smart doorbell\u2019s companion app. (If you own a Nest Doorbell<\/a>, go into the Nest Aware settings in your Google Home app. I don\u2019t know why Google hides this stuff.)<\/p>\n

Delete any misidentified faces that your doorbell has captured, and be sure to tell your doorbell the names of any unidentified people that you expect to return to your home. If you regularly curate this database, your doorbell should get a lot better at identifying guests.<\/p>\n

Here\u2019s the bad news; even if you try to improve the accuracy of your doorbell\u2019s facial recognition system, it\u2019ll never be perfect. In fact, it may always suck. Facial recognition technology is still pretty rudimentary, and smart doorbells use very basic hardware to \u201csee\u201d people.<\/p>\n

If you hate the idea of curating a facial recognition database, maybe you should just disable the feature. Replacing your doorbell isn\u2019t worth the money, as every brand\u2019s facial recognition systems suffer from the same problems.<\/p>\n<\/div>\n