New class action filed against Guthy-Renker
Claiming that she has lost one-third of her hair after using Guthy-Renker LLC WEN Cleansing Conditioner hair-care products, a Florida resident has filed a putative nationwide class action in a California federal court, alleging strict products liability, breach of warranty, failures to warn and test, as well as consumer fraud. Friedman v. Guthy-Renker LLC, No. 14-6009 (U.S. Dist. Ct., C.D. Cal., filed July 31, 2014).
The complaint includes what are alleged to be a small sample of numerous blog and other Website statements from WEN conditioner purchasers making the same comments about hair loss that continued even after use of the product ceased. It further asserts that “YouTube features numerous videos also documenting hair loss caused by WEN Cleansing Conditioner.” The plaintiff claims that the company not only failed to warn about the product defects, but “actively concealed customers’ comments concerning hair loss, by blocking and/or erasing such comments from the WEN Facebook page.” She also alleges that the company makes false statements on which she relied about the “gentle nature of the product” including that it can be used every day.
Video face tracking: a work in progressThe reported average frame rate is 6fps on a Pentium IV 3GHz processor.
Object trackers of all the above families use a variety of features, and selecting the right features plays a critical role in this context. The uniqueness of a feature is key for easily distinguishing objects in the feature space. For face tracking, local descriptors, such as gradients and histograms of gradients, are present in much of the relevant work in the area. Color is also an important feature, since the tone of skin, hair and other facial attributes are, up to a certain degree, distinctive from the tones of other regions normally found in a scene. Besides these basic features, other face-tracking schemes may consider appearance-based models or hybrid feature sets.
For video indexing and search tasks, face tracking is often used together with clustering or other non-supervised techniques. For example, in 2013 Zhang and coworkers presented a system to extract temporal face sequences from videos and group them into clusters, with each cluster containing video clips of a same person. Their system employs face detection (to locate an initial occurrence of a face) and bi-directional (i.e., forward and backward) face tracking. The face regions found in these two ways are combined into a temporal face sequence, from which representative faces are selected based on face image qualities. (A face sequence may contain too many face variations for clustering). Next, the system extracts appearance and temporal features from the representative faces and performs a similarity analysis. Finally, face sequences belonging to the same person are grouped by a semi-supervised agglomerative clustering, taking as input a similarity matrix resulting from the previous step.