Jay McGavren’s Journal

17,993 files, most of them .jpg and .avi…

SVN repo: 36.8 GB
.git directory: 30.1 GB (Git wins)
SVN working copy: 47 GB
Git working copy: 23.5 GB (Git wins a LOT.)

Passed on by Jeremy:

You answer the following 12 questions about yourself.

1. What is your first name?
2. What is your favorite food?
3. What high school did you attend?
4. What is your favorite color?
5. Your celebrity crush?
6. Favorite drink?
7. Dream vacation?
8. Favorite dessert?
9. What do you want to be when you grow up?
10. What do you love most in life?
11. One word to describe you?
12. Your Flickr name?

Type your answer to each of the above questions into Flickr’s search. Using only the images that appear on the first page, choose your favorite and copy and paste each of the URLs into the Mosaic Maker (3 columns, 4 rows).

1. Bill Gates and Jay Z, 2. Sweet & Sour Pork, 3. Brendan Benson at the Southgate House, 4. green & blue, 5. Halle Berry, 6. Pepsi Billboard on Hudson River (Manhattan), 7. Kalalau Lookout - Kauai, Hawaii, 8. Hersheys Chocolate Cake 2, 9. DataArt Software Outsourcing Developer (fun), 10. Wife, listen to me!, 11. Energetic, 12. Gamer

Finally! Picasa Web Albums rolled out its facial recognition feature. No equivalent for the desktop Picasa (yet), sadly.

It’s fast, and accurate. Some interesting bits:

-Most of the time, it has no trouble distinguishing between my pre-beard and post-beard face.
-It thinks my wife and my-wife-with-sunglasses are two separate people.
-It thinks my-wife-with-sunglasses and my-son-with-sunglasses are the same person. :)
-Pictures of a rapidly-growing kid (age 3-5) seem to make it uncertain, though its “most likely” match is usually correct.
-Based on only one picture of my brother at age 26, it just picked out a picture of him at age 11! (It wasn’t certain, but he was the most-likely match.)
-A fake sneer or frown tripped it up (though again, the most-likely match was correct).
-A subject losing (or gaining) a considerable amount of weight makes it uncertain.
-The lighting on a face has to be REALLY poor to trip it up.
-Most-likely match was right even on a face that was ~20% blocked.
-Good guess on a face viewed through smoke (from a yellow smoke bomb).
-Two different women with similar makeup, facial expression and pose were sorted next to each other. (And the second was mis-identified as the first, probably because she usually wears glasses and wasn’t in this shot.)
-The best guess is usually of the same sex, but when it’s tripped up, members of the opposite sex may outrank the actual subject for second- and third-best guesses.
-Subjects of the same (or similar) ethnicity tend to get grouped together.
-Sticking your tongue out is a good way to trip it up.
-Odd angles (like up from the floor) confuse it (badly). Profile-views aren’t as bad, though.
-(Obviously) cartoon faces occasionally find their way into the list.
-A short-lived goatee only throws it off once; after that the best guess is correct for further goatee pictures.
-Bangs partially covering the eyes throw it off.

I know some of these are “duh” items, but this has just made me appreciate what a hard problem facial recognition is. Especially in the area of photos, where you’re dealing with changes in age, weight, hair, expression, poor lighting, sensor noise, and a host of other problems.

Now, how can I sort the photos back on my hard drive, since Google and I have done all this work?

Insane storm tonight - lightning every half second, and the thunder is a constant roar instead of single bursts. Got a couple pictures…

I got this one…

And Diana got this…

Our cactus in the backyard produces huge, beautiful blooms, but only at night, only for a couple weeks in summer, and each flower is gone by morning. This time lapse was made the hard way - by setting up a tripod, then going out back every five minutes to press the shutter button.