Prozesse der Emotionsregulierung in psychoanalytischen Langzeitpsychotherapien

ZusammenfassungDas Projekt verfolgt das Ziel, das affektive Regulierungsgeschehen in psychotherapeutischen Interaktionen anhand von Videoaufnahmen mikroanalytisch zu beschreiben und mit einem produktiven therapeutischen Prozess in Beziehung zu setzen. Analysiert werden mimische Verhaltensweisen, die mit dem Facial Action Coding System (FACS) objektiv erfasst werden. Im Fokus des Beitrags stehen die sogenannten Prototypischen Affektiven Mikrosequenzen (PAMS). PAMs sind durch Lächeln und Lachen gekennzeichnet und dienen dazu, Störungen in der Affektregulierung mit Hilfe des Gegenübers auszuregulieren. Sie spielen in der therapeutischen Beziehung eine bedeutsame Rolle bei der Aufrechterhaltung einer Balance zwischen Beziehungssicherheit und Konfliktspannung. Unsere Analysen sollen dabei helfen, die Funktionen dieser weitgehend unbewussten Prozesse besser zu verstehen und für den therapeutischen Alltag nutzbar zu machen.

Facial Action Coding System Applied to Criminal Investigations: The Analysis of a Homicide Case in Southern Italy

Recent criminal psychology research has raised critical questions about applying non-verbal communication methods for lie detection purposes in forensic settings. Research has shown low correlations between non-verbal communication and deception. However, non-verbal communication methods are still widely applied and suggested by police manuals. Results obtained by experimental and field research are biased by the following factors: (i) attention is given only to quantitative aspects of non-verbal behavior; (ii) there is a lack of research of qualitative aspects related to non-verbal behavior analysis; (iii) lack of connections between non-verbal indicators and verbal content; (iv) lack of attention on timing of non-verbal behavior; (v) most research is performed on psychology students in experimental contexts. This article proposes a new methodology for applying the Facial Action Coding System as investigative support and not as a lie detection method. The Facial Action Coding System will be introduced to integrate with verbal content analysis and a new framework to interpret non-verbal signs discussed. The aid of standardized non-verbal methods will be discussed through an in-depth psychological analysis of a case of homicide perpetrated in 2010 in Southern Italy by discussing a video analysis of the suspects’ statements.

Sintesa Ekspresi Wajah Karakter Virtual 3D menggunakan Action Unit berbasis Facial Action Coding System (FACS)

Ekspresi wajah pada karakter virtual 3D memegang penran penting dalam pembuatan sebuah film animasi. Untuk mendapatkan ekspresi wajah yang diinginkan seorang animator kadang mengalami kesulitan dan membutuhkan waktu yang tidak sedikit. Penelitian ini dilakukan untuk mendapatkan ekspresi wajah dengan menggabungkan beberapa Action Unit yang ada pada FACS dan diimplementasikan pada wajah karakter virtual 3D. Action Unit pada FACS dipilih karena mengacu pada struktur otot wajah manusia. Eksperimen yang dilakukan menghasilkan komninasi Action Unit yang dapat membentuk ekspresi seperti joy expression yang dihasilkan dari kombinasi AU 12+26, dan surprise expression yang dihasilkan dari kombinasi AU -4+5+26. Sedangkan untuk sadness expression dan disgust expression karena ada AU yang tidak terwakili pada model 3D sehingga di dapatkan hasil ekspresi yang kurang maksimal.

Automatic recognition of macaque facial expressions for detection of affective states

Internal affective states produce external manifestations such as facial expressions. In humans, the Facial Action Coding System (FACS) is widely used to objectively quantify the elemental facial action-units (AUs) that build complex facial expressions. A similar system has been developed for macaque monkeys - the Macaque Facial Action Coding System (MaqFACS); yet unlike the human counterpart, which is already partially replaced by automatic algorithms, this system still requires labor-intensive coding. Here, we developed and implemented the first prototype for automatic MaqFACS coding. We applied the approach to the analysis of behavioral and neural data recorded from freely interacting macaque monkeys. The method achieved high performance in recognition of six dominant AUs, generalizing between conspecific individuals (Macaca mulatta) and even between species (Macaca fascicularis). The study lays the foundation for fully automated detection of facial expressions in animals, which is crucial for investigating the neural substrates of social and affective states.

Visual Depression Diagnosis From Face Based on Various Classification Algorithms

Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.

An Analysis of ‘Emotions’ in Transgender through Facial Action Coding System

Transgender is a blanket term wrapping individuals whose gender identity, expression or behavior transgresses their biological sex. They are often put on the periphery in terms of finances and the social inclusion. The varying stereotypes of sexual binary recognize the transgenders as socially misfit and economically unaccepted. Emotionally, the transgenders face hardships and lack of social support that push them at the social cross-roads in terms of denial and rejection. Nevertheless, this emotional distress is generally aggravated by the family, friends and acquaintances. This paper examines the emotional binary of Transgenders and parents after their detachment by using an automatically based system on facial gestures called Facial Action Coding system (FACS). Further, their affirmative emotions, such as, Happiness, Sadness, Anger, Disgust, Contempt, Surprise, and Fear is rated with an intensity rate justifying the strength of the respective emotion. The FACS analysis of emotion of sadness resulting in depression is evaluated by using 20-item measure of the Center for Epidemiologic Studies-Depression Scale (CES-D). The paper also explores the facilitative coping experiences after the recognition of the sexual identity by noting down the broad scale of emotional bandwidth. However, facial expressions of transgender respondents and their parents are recorded and are selected based on snow ball sampling. The research work has analyzed emotions of transgender respondents and their parents to know the ground reality of real troubles they come across standing on periphery of the society.