Recognition of Face Identity and Emotion in Expressive Specific Language Impairment

Objective: To study face and emotion recognition in children with mostly expressive specific language impairment (SLI-E). Subjects and Methods: A test movie to study perception and recognition of faces and mimic-gestural expression was applied to 24 children diagnosed as suffering from SLI-E and an age-matched control group of normally developing children. Results: Compared to a normal control group, the SLI-E children scored significantly worse in both the face and expression recognition tasks with a preponderant effect on emotion recognition. The performance of the SLI-E group could not be explained by reduced attention during the test session. Conclusion: We conclude that SLI-E is associated with a deficiency in decoding non-verbal emotional facial and gestural information, which might lead to profound and persistent problems in social interaction and development. Copyright (C) 2012 S. Karger AG, Base

A comparative analysis among dual tree complex wavelet and other wavelet transforms based on image compression

Recently, the demand for efficient image compression algorithms have peeked due to storing and transmitting image requirements over long distance communication purposes. Image applications are now highly prominent in multimedia production, medical imaging, law enforcement forensics and defense industries. Hence, effective image compression offers the ability to record, store, transmit and analyze images for these applications in a very efficient manner. This paper offers a comparative analysis between the Dual Tree Complex Wavelet Transform (DTCWT) and other wavelet transforms such as Embedded Zerotree Wavelet (EZW), Spatial orientation Transform Wavelet (STW) and Lifting Wavelet Transform (LWT) for compressing gray scale images. The performances of these transforms will be compared by using objective measures such as peak signal to noise ratio (PSNR), mean squared error (MSE), compression ratio (CR), bit per pixel (BPP) and computational time (CT). The experimental results show that DTCWT provides better performance in term of PSNR and MSE and better reconstruction of image than other methods