Literature review (part 8)

"The role of eyebrows in face recognition" by Sadr et al.: They perform a lesion study, removing either eyes or eyebrows from images of celebrity faces where the task is to identify the celebrity. They find recognition performance for humans is worse with no eyebrows than it is with no eyes.

They speculate as to why eyebrows may be important to recognition: 1) Since eyebrows are used to convey emotion, humans may naturally attend to them more, giving them higher weight in recognition, 2) Eyebrows are stable across lighting and image degradations, as they are large and high-contrast. Thus they are relatively stable and so good low-noise information sources.

The paper contains some interesting references:

"Given that the brow ridge may have been an important, sexually distinctive characteristic of our early ancestors' faces, it is not surprising that recent studies have found an important role for eyebrow thickness in discriminating between male and female faces (Bruce et al 1993)" pg 286
This suggests eyebrows as useful for gender discrimination with computer vision. If we consider eyebrows external features, determining gender is half the battle.

"In fact, research in the latter field has shown the eyebrows to be, in a kinematic sense, the most expressive part of the face (Linstrom et al 2000) and, we would suggest, a facial feature whose gestures would be easily recognized at a distance." pg 292
Just reinforcing the idea that eyebrows are good for low-visibility recognition.

"New appearance models for natural image matting" by Singaraju et al.: The task is image alpha matting using sparse trimaps. They build on Levin et al.'s work, but address a failure case of that work, where the model could overfit when the foreground and background layers are locally linear. Like Levin's, their solution is closed-form. They compare to Levin, and claim to get better matting results.

"Estimation of Alpha Mattes For Multiple Image Layers" by Singaraju and Vidal: Alpha matting typically assumes two layers, a foreground and a background. This work addresses (sparsely initialized) alpha matting for 2 or more layers. They use as an example two toy trolls standing next to each other, with overlapping hair.

The technique they propose is closed-form, but it does not necessarily produce alpha values which fall in the unit interval, breaking the traditional probabilistic interpretation of matting. When they add the constraint that the alpha values fall in the unit interval, they lose the closed form solution. However, the optimum value can still be obtained by solving a quadratic program.