The study and development of software able to show the effect of aging of faces is one of the tasks of face recognition technologies. Some software solutions are used for investigations, some others to show the effects of drugs on healthy appearance, however some other applications can be proposed for the analysis of visual arts. Here we use a freely available software, which is providing interesting results, for the comparison of ancient marble busts. An analysis of Augustus busts is proposed.Face recognition software and technologies are focusing on the possibility of computer algorithms to recognize a face in some galleries of images, which can be acquired from pictures provided by still images or frames of a video sequence. The software algorithms then must reproduce the innate human ability to recognize a face, a fundamental task in mimic the behavior of the human brain. Another important research in the field of face recognition is the study of the effects of aging in the craniofacial morphology [1]. Human faces change during the life, their features varying affected by several factors ranging from the inherent genetics and the environmental constrains.As told in Ref.2, several agencies of investigation regularly require matching a probe image with the individuals in the missing person database. However, there are often significant differences between facial features of probe and gallery images due to age variation. For instance if the probe image is a 15 years-old boy or girl and the gallery image of the same person is of 5 years, the face recognition algorithm must perform a very difficult task.Researchers have then proposed several age simulation and modeling techniques [2]. These models alter the face according to the facial growth over a specific period of time. The reader can find several references given in [2], the oldest is that of Burt and Perrett [3]. Since the face aging is affecting the performance of face recognition systems, the analysis of synthetically generating age-progressed or age-regressed images is a good method of improving the robustness of face-based biometrics [1]. In Ref.4, the accuracy of methods for the security of biometric verification systems is investigated. The paper presents methods of modeling and predicting facial template aging based on matching score analysis. An interesting social application of a software solution for aging faces was proposed by the Task Force for Tobacco-Free Women and Girls in New York State, which utilized it to illustrate how smoking can affect the facial appearance [5]. The task force members reviewed the literature on the association between smoking and facial wrinkling, provided parameters for customization of the APRIL (age progression image launcher) [6]. Photoshop is also used for ageing the faces, using its FaceAge® plugin. However, this is not freely available. Some software solutions are then used for investigation, some others to show the effects of drugs on healthy appearance, however other applications can be imagined and used, for i...

Purpose: Most tobacco cessation studies in cancer patients rely upon retrospective reviews in a small subsample of cancer patients presenting for treatment. The purpose of this study is to describe the tobacco use and cessation patterns for all patients (100% sampling) presenting to a thoracic oncology clinic at a NCI Designated Comprehensive Cancer Center using a standardized assessment and automatic tobacco cessation referral program. Methods: Patients presenting to the thoracic oncology clinic at Roswell Park Cancer Institute (RPCI) were screened with a standardized tobacco assessment and all patients who used tobacco within the past 30 days were automatically referred to a dedicated tobacco cessation program. Demographic and health information were obtained from the electronic medical record, as well as the RPCI tumor registry, for all thoracic patients referred to the cessation program between October 2010 and October 2012. Tobacco information was collected by the cessation specialist. Descriptive and multivariate analyses were used to identify significant associations between demographics and disease characteristics with participation rates and self-reported quit rates. Results: Among the 978 patients referred to the cessation program, 531 (54.2%) had information in the tumor registry. 476 (89.6%) patients had a form of lung cancer and 55 (5.6%) had another thoracic cancer, such as esophageal, bronchial, thymus, mediastinum or pleura cancer. 226 out of the 531 (42.6%) patients with tumor registry information were deceased, of whom the majority died from the primary thoracic cancer or complication from that cancer (N=151/226; 66.8%). Among those who were deceased, those who self-reported former tobacco use status (not currently using) at the first visit had a significantly longer survival time (n=53; mean=20.40 months, SD=30.91) compared to current users at first visit (n=157; mean=13.75 months, SD=15.41; p=0.042). Change in quit status from diagnosis to the first contact was not statistically associated with survival outcomes after controlling for age, packyears, sex and clinical stage of disease. Compared to being a current tobacco user at diagnosis and first contact, a significant difference in survival was not observed among those who quit between diagnosis and the first contact by the cessation service (HR=1.16, 95%CI: 0.63-2.13), those who relapsed between diagnosis and the first contact (HR=0.80, 95% CI: 0.36-1.79), and those who self-reported being quit at diagnosis and the first contact (HR=0.61, 95% CI: 0.28-1.34). However, age (HR=1.03; 95% CI: 1.01-1.05); being male compared to female (HR=1.64; 95% CI: 1.07-2.51) and clinical stage of disease (clinical stage compared to stage 1: HRstage2=2.20; 95% CI: 0.84-5.78; HRstage3=3.48; 95% CI: 1.67-7.24; HRstage4=8.49; 95% CI: 4.35-16.58) were significantly associated with survival. Although the association of quitting after diagnosis and survival was not statistically significant, the number of those who quit was small, limiting statistical power. Conclusions: Patients who participated in an automated institutional cessation program and who quit smoking may have a reduced risk of death. Citation Format: Katharine Dobson Amato, Mary Reid, Robert Reed, Patricia Hysert, Robert Hysert, Stephanie Segal, Gary Giovino, Maansi Travers, Heather Ochs-Balcom, Michael Zevon, Kenneth Michael Cummings, Chukwumere Nwogu, James Marshall, Martin Mahoney, Andrew Hyland, Graham Warren. Effects of automated tobacco assessment and cessation on survival for thoracic cancer patients. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr B50.