If there is one feature on Facebook which delivers “social utility” magic even to the most average of users, it’s Photos. In fact the feature is so popular that by Facebook’s own account 1 billion photos are uploaded every month—a staggering number that makes it the largest photo site on the Web. However, as with all good things, there are also drawbacks, and in this case discovery is high on the list. While Facebook makes it super easy to discover photos in which you were tagged, there is no chance that every one of those billion photos are tagged each month. And that leaves a big opportunity.

Let me put it another way: How many photos of you are there on Facebook that you’re completely unaware of? Israeli-based Face.com will help you find them with ‘Photo Finder,’ a Facebook app that uses facial recognition to help members locate untagged photos of themselves and their friends.

Once installed, the app will begin scanning you and your friends’ photo albums, a process that requires a bit of time to complete, but the welcome screen will immediately display photos that were ‘Auto Tagged’. Users can either accept, decline or identify the correct individual themselves. The only users that have veto power to alter or decline a tag are the person who uploaded the photo and the person tagged.

Facial recognition technology is taking off. Competing technology can be found in both Apple’s iPhoto and Google’s Picasa, but those are limited to searching only your personal collection of photos (although iPhoto lets you upload them with the tags to Facebook). With Photo Finder, you are not limited to your own photo collection. Users can search manually for photos of friends or browse for recently tagged ones. Users can also track specific users by flagging them for the “Watch List”. Photo Finder will prompt hits via Facebook’s ‘Notification’ window.

The facial recognition technology was developed from scratch by the Face.com team over a year and half. It was designed from the ground up as a low-cost platform to meet two specific requirements: The first is recognition of “Faces in the Wild”. This applies to everyday photos that suffer from such issues as low resolution or bad lighting, or where faces are obscured with sunglasses, for example. The second requirement is to have the technology be scalable. In this respect Face.com claims to be able to perform facial recognition on all one billion photos currently uploaded into Facebook every single month using only a few machines.

Photo Finder scans the photos of users and all their friends, along with “other albums in your wider network where there’s a high likelihood of your (or friends’) appearances.” To understand the sheer volume of backend work required, consider the following statistics: The first 150 users in Face.com’s system required 20 million photos to be scanned, resulting in 30,000 identified faces. My personal installation of the app required it to scan 79,449 photos which resulted in 11,933 tags of myself and my friends. Photo Finder will then go back and re-scan the albums after its initial scan to identify newly added photos.

The social tagging feature within Facebook Photos gives Face.com a major boost because it can use those tags to train its system. It is important to note that Photo Finder does not add or alter Facebook’s own photo tags. Tagging that occurs through the app is stored in metadata accessible through the Photo Finder app alone. Also noteworthy is the fact that from a privacy perspective Photo Finder piggy-backs on the users’ Facebook settings and does not alter them in any way. Also, none of the photos are stored on Face.com’s servers. These only perform the heavy lifting required for the facial recognition and the storing of tags added through the app.

Even though this is an Alpha version of the app and there are occasional bugs, it works remarkably well. I was quite surprised that it was able to correctly identify individuals in side shots, backgrounds, or in extremely poorly lit photos. It all depends on the amount of photos available, but as a rule of thumb the Face.com team aims for 90% accuracy. It seems that they have some real technology on their hands as evidenced by their scoring first place in the “Labeled Faces in the Wild” experiment conducted by the University of Massachusetts’ Computer Vision Laboratory (Face.com are identified as ‘Hybrid descriptor-based’ in the linked paper).

It’s clear that Photo Finder was designed for mainstream viral appeal and I must admit that I found the app to be VERY addictive, spending at least ten minutes tagging people every time I played with the app over the course of the past two weeks. I have a hunch that once made publicly available the app is going to be incredibly popular on Facebook.

This post was originally posted on TechCrunch.com where I cover the Israeli startup scene.

If you haven’t heard of Kutiman yet you’re about a week late on the latest music sensation to be incubated on the Web. Ophir Kutiel, aka Kutiman, is an Israeli musician and producer that released a project titled Thru You on the Web seven days ago. It has since garnered over a million views and generated a buzz both on the blogosphere and on Twitter.

The project consists of seven music tracks/videos that are made exclusively from video material found on YouTube. Kutiman spent 3 months in his bedroom splicing and dicing over one hundred videos for samples of singers and instruments—from guitars, pianos, drums and harps, to synthesizers, a bouzouki and even a cash register.

The resulting seven tracks which range in genres—from R&B, Funk and Reggae, to Jungle, Afro and Jazz—are quite impressive. The project as a whole is reminiscent of DJ Shadow’s Endtroducing….., a brilliant and seminal album created completely by the sampling of other albums (hear it here).

Apart from the revolutionary music creation aspect, this story also has an interesting social media angle. The entire snowball effect that resulted in over a million views, a crashed website and a fair bit of buzz, was initiated by three people associated with the project. They emailed twenty people in total and it took a life of its own from there. From zero views to over a million in less than 7 days with no marketing dollars, blackhatting or SEO’ing involved. The team around Kutiman attribute much of this to word traveling across Twitter. If this is true, Kutiman may in fact be the first music star to be born on Twitter. There’s no question that we are sure to see other up and coming musicians harness it as well in the future.

Here are a couple of Kutiman’s tracks:

Casual games may see a vast amount of traffic, but monetizing them can be more than a little tricky due to issues relating to Flash-based game files and the needs of various publishers. Israeli startup TicTacTi is looking to make monetizing casual games more efficient, by using image recognition to insert ads into casual gaming widgets.The biggest obstacle in providing In-Game Advertising (IGA) typically involves getting the actual ad into the game. Games, which are typically in Flash SWF format, require distribution by a publisher, which can be anything from an Oberon, to a HeyZap, to an online edition of a newspaper. Each publisher has its own quirks and demands when it comes to monetization—one wants to advertise pre-game, the other post, and the third between levels. And this is where the crux of the problem lies—all of these quirks require alternate versions of the game source for the various publishers and advertisers.

TicTacTi realizes that requiring developers to integrate with multiple SDK’s to facilitate the embedding of ads is not scalable, so it developed a semi-manual method that at least takes the SDK integration out of the equation.

Each game has to be set up by TicTacTi, a process the company estimates at about one to two hours per game. The actual game source code is not required which means that games can by encrypted—an important point for game developers. It’s here that TicTacTi “marks” events in games where ads could be placed. For example, a game could be marked in such a way that when the “Loading” prompt is visible, it would initiate a pre-roll ad marker, and when the “Game Over” prompt is visible, it would initiate a post-roll marker.

TicTacTi’s image recognition engine seeks these visual events in order to trigger the ad insertion. If the game source already includes TicTacTi’s IGA logo marker (see right), the game preparation stage can be skipped altogether because the image recognition engine will identify it automatically.

The image recognition is performed entirely client-side with ActionScript. The patent-pending technology involves a mechanism that combines image recognition throttling and emulation. This means that it is activated for small segments of time so as not to impose a cost on the user’s CPU. TicTacTi’s own testing revealed CPU usage remains the same for the entire game duration.

In order to embed the game, the publisher would call TicTacTi’s wrapper, which would in return load the game, along with additional elements. These include the ones that drive the image recognition, the ad insertion component and the reporting to the backend.

Standard ad units and tags are supported so ads inserted into the Flash games can originate from ad exchanges such as Right Media, Double Click, or the publisher’s own ad server. TicTacTi will charge a varied commission for the service.

This post was originally posted on TechCrunch.com where I cover the Israeli startup scene.

Buying a ticket to a live event, be it sports, music, or theater, is a piece of cake on the Web. There are online services galore that help users find and purchase tickets at their desired price. But does the price you pay actually equate to the true value of the ticket? Put differently, could a ticket for a different location in the same venue for the same price (or even less) be the better one? Say hello to SeatKarma.com which believes that the true value of a ticket is a combination of its price and its location in the venue. The company’s tagline: “No two tickets are the same!”

SeatKarma’s search engine covers 99% of tickets available for purchase online by retrieving live ticket information from a couple of hundred secondary market ticket brokers. The cost comparison is then augmented with venue mapping available for approximately 1600 venues. 1300 of these are “live maps” which place a marker on the section where the seat will be located. The remaining 300 are small venues such as bars where seat mapping doesn’t apply. The company claims it now has more live maps than any other comparison engine on the market.

SeatKarma has a really cool feature where 140 venues in its system have actual “Court View” photos of the perspective you’ll have of the court or field from your desired seating location. These 140 venues include all the US professional sports venues for the NBA, NFL, MLB, NHL, as well as the some of the top NCAA football venues.

There are also twenty 3D maps that were custom drawn for theater venues with complicated seating arrangements (balconies, Mezzanine levels, etc). More will be added over time. Eventually SeatKarma plans to have all the major venues drawn in 3D as well. Baseball stadiums are also currently being mapped in 3D and are due to be available on the site in time for the start of the new baseball season April 1st.

The company’s business model is based on affiliate commissions generated by selling tickets through brokers. Ten percent of the revenue will be given to charity every month.

Here’s the killer part: SeatKarma is just a couple of MBA graduate students from Texas A&M University—Ohad Nezer and Chris Nicolaysen—that pulled all of this off for only $30K… You just gotta love that!

This post was originally posted on TechCrunch.com where I cover the Israeli startup scene.