Correlating Jaw Bone Growth and Nasal Morphology Among Adult Patients With Cleft Lip and Palate: A Retrospective Cephalometric Study.

This retrospective cross-sectional study reviewed adult patients with operated cleft lip and/or palate (CL/P) and normal control, and performed comprehensive craniofacial and nasal morphological analyses based on lateral cephalometric radiographs. Pearson or Spearman correlation coefficient assessed intraclass correlation. Seven hundred fifty-seven operated patients with CL/P, and 165 noncleft normal controls were enrolled.

A retrospective cephalometric study on the craniofacial morphology of adult patients with unoperated submucous cleft palate.

This retrospective cross-sectional study reviewed adult patients with unrepaired SMCP, OCP and normal control and performed comprehensive skeletal and soft tissue morphological analyses basing on lateral cephalometric radiographs. One way-ANOVA and rank-sum tests detected potential intergroup differences. 32 subjects with unrepaired SMCP, 42 with unrepaired OCP and 28 noncleft normal controls were enrolled.

Pearls and pitfalls in contemporary management of marginal velopharyngeal inadequacy among children with cleft palate.

Marginal velopharyngeal inadequacy (MVPI) is a particular status of velopharyngeal closure after cleft palate repair. The physiological and phonological characteristics of patients with MVPI are significantly different from those with typical velopharyngeal insufficiency. The pathological mechanisms and diagnostic criteria of MVPI are still controversial, and there is limited evidence to guide the selection of surgical and non- surgical management options and a lack of recognized standards for treatment protocols.

A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment.

Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits.

Weighted discriminative collaborative competitive representation for robust image classification.

Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific representations, which is very critical for strengthening the pattern discrimination of collaborative representation (CR).

Geometry Guided Pose-invariant Facial Expression Recognition.

Driven by recent advances in human-centered computing, Facial Expression Recognition (FER) has attracted significant attention in many applications. However, most conventional approaches either perform face frontalization on a non-frontal facial image or learn separate classifier for each pose. Different from existing methods, this paper proposes an end-to-end deep learning model that allows to simultaneous facial image synthesis and pose-invariant facial expression recognition by exploiting shape geometry of the face image.