Who should use the Analyze facial expressions workflow?
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Marketing
Practical execution plan for analyze facial expressions with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Validated emotion analysis with improved accuracy and confidence in recommendations
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Validated emotion analysis with improved accuracy and confidence in recommendations
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Loom to a curated, clean dataset of facial expression media ready for analysis. Then, you pass the output to Google AI Gemini API & MediaPipe to aligned face crops with landmarks for each detected face in the dataset. Then, you pass the output to Affectiva to emotion labels and confidence scores for every face or frame in the dataset. Then, you pass the output to Gemini 2.5 Pro to a structured table linking facial expressions to marketing context with summary statistics. Then, you pass the output to Tableau AI to a final report with visualizations and actionable marketing recommendations based on facial expression analysis. Finally, Keymakr is used to validated emotion analysis with improved accuracy and confidence in recommendations.
Collect and prepare facial expression data
A curated, clean dataset of facial expression media ready for analysis
Detect faces and extract key landmarks
Aligned face crops with landmarks for each detected face in the dataset
Classify basic emotions using a pre-trained model
Emotion labels and confidence scores for every face or frame in the dataset
Correlate expressions with marketing context
A structured table linking facial expressions to marketing context with summary statistics
Generate actionable insights and visualizations
A final report with visualizations and actionable marketing recommendations based on facial expression analysis
Validate and iterate (optional)
Validated emotion analysis with improved accuracy and confidence in recommendations
Gather video or image files containing faces relevant to your marketing context (e.g., customer reactions to ads, product unboxing videos, or focus group recordings). Ensure each file is in a supported format (MP4, JPG, PNG) and that faces are clearly visible. Preprocess by cropping or resizing to standard dimensions (e.g., 224x224 pixels) and converting to grayscale if required by your chosen tool.
Why Loom: Loom provides screen and camera recording to capture video data, and its transcription-based editing helps prepare footage for preprocessing.
Use a face detection model (e.g., MTCNN, Dlib, or MediaPipe) to locate faces in each frame or image. Extract facial landmarks (e.g., eyes, nose, mouth corners) to normalize for head pose and alignment. This step ensures consistent input for emotion classification.
Why Google AI Gemini API & MediaPipe: Google AI Gemini API & MediaPipe includes MediaPipe, a leading library for face detection and landmark extraction.
Feed each aligned face crop into a pre-trained emotion classifier (e.g., FER2013-based CNN, DeepFace, or Azure Face API) that outputs probabilities for seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. Record the dominant emotion per face along with confidence scores.
Why Affectiva: Affectiva specializes in emotion recognition and facial expression analysis, directly matching the need for a pre-trained emotion classification model.
Map the detected emotions to specific marketing stimuli (e.g., ad timestamp, product feature shown, or survey question). Use the metadata from Step 1 to segment results by audience, campaign, or moment. Calculate aggregate metrics like average happiness per ad variant or surprise peaks during key moments.
Why Gemini 2.5 Pro: Gemini 2.5 Pro can perform complex multi-step reasoning and data analysis, suitable for correlating expressions with marketing context.
Create charts (e.g., emotion distribution bar charts, heatmaps over video timeline) and a written summary highlighting which stimuli triggered positive/negative reactions. Recommend specific marketing actions (e.g., 'Use Ad Variant B because it sustained higher happiness scores'). Export the report as a PDF or dashboard.
Why Tableau AI: Tableau AI provides data visualization and predictive modeling, ideal for generating actionable insights and visual reports.
If time permits, cross-validate emotion classifications with human raters or alternative models (e.g., using a different FER model). Adjust preprocessing or model thresholds based on misclassifications. Re-run analysis on a subset to confirm robustness before finalizing recommendations.
Why Keymakr: Keymakr provides image and video annotation services, which are essential for human validation and labeling of expression data.
§ Before you start
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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