Real‑Time Facial Emotion Detection Using OpenCV and CNN with

Music Playback System

B. Venkata Charan Kumar, N. Venkatesh Naik, B. Sumanth, N. Mahaboob Hussain,

B. Sai Charan and P. M. D. Akram

Department of Computer Science and Engineering (Data Science), Santhiram Engineering College, Nandyal, Andhra

Pradesh, India

Keywords: Affective Computing, Deep Learning for HCI, Adaptive Multimedia Systems, Computer Vision Applications,

Real‑Time AI.

Abstract: Contemporary human-computer interfaces increasingly demand affective computing capabilities to interpret

user states. This research presents an innovative framework combining computer vision techniques with deep

learning to achieve real-time facial emotion analysis, subsequently driving an intelligent music

recommendation engine. Our architecture employs OpenCV for facial feature extraction and a custom

convolutional neural network for emotion classification, achieving 91% accuracy under optimal conditions.

The integrated music subsystem utilizes audio feature extraction and sentiment analysis to dynamically select

contextually appropriate tracks. Comprehensive testing reveals significant improvements in user engagement

metrics (20% enhancement) and system responsiveness (45% latency reduction post-optimization). Future

directions include implementing transformer architectures for improved micro-expression recognition and

developing federated learning approaches to address privacy concerns. This work bridges critical gaps

between affective computing and personalized media delivery systems.

1 INTRODUCTION

1.1 Background and Motivation

The proliferation of intelligent systems has created

unprecedented opportunities for emotionally aware

human-computer interfaces. As psychological studies

confirm, facial expressions constitute approximately

55% of human emotional communication

(Mehrabian, 1971), making automated facial affect

recognition a cornerstone of modern affective

computing. Recent breakthroughs in deep neural

networks, particularly convolutional architectures,

have enabled unprecedented accuracy in real-time

emotion classification tasks. These newly parallel

developments in multimedia recommendation

systems have allowed for contextually aware content

delivery, providing a synergistic potential for

emotionally intelligent interfaces.

1.2 Problem Statement

This contrasts with traditional music recommenders

which use historical like-dislike graphs rather than

present timely emotional states. Recognizing faces is

made more complex by differences in illumination

and facial obstructions. The majority of systems

reduce affect to 5-7 basic emotions, ignoring more

subtle states.

Figure 1: Emotion Detection and Play Music Workflow.

Kumar, B. V. C., Naik, N. V., Sumanth, B., Hussain, N. M., Charan, B. S. and Akram, P. M. D.

Real-Time Facial Emotion Detection Using OpenCV and CNN with Music Playback System.

DOI: 10.5220/0013882700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

337-343

ISBN: 978-989-758-777-1

337

1.3 Objectives of the Study

The present investigation seeks to achieve four main

objectives:

• To engineer a real-time emotion classification

system with <200ms latency.

• To develop an emotion-to-music mapping

ontology incorporating acoustic features.

• To optimize system performance across

heterogeneous hardware platforms.

1.4 Contribution of the Study

• Novel Architecture: First implementation of a

knowledge-guided CNN for FER.

• Dynamic Adaptation: Real-time music tempo

adjustment based on emotional flux.

• Computational Efficiency: 40% parameter

reduction versus baseline ResNet-50.

• Figure 1 shows the Emotion detection and play

music workflow.

2 LITERATURE REVIEW

Kumar P et al., (2023) strike a meaningful trade-off

between speed and efficiency, being suitable for

using in things like responsive AI assistants or, even,

wearable devices. They dramatically lower

computation required, allowing for quicker

processing and longer battery life, a key requirement

for usage in the wild. But too much compression can

sacrifice accuracy, especially at the edges of complex

or subtle emotions, which could lead to

misinterpretation of subtle emotions in real-world

situations. The hardware aspect also plays a role in its

performance, and some developers may not have

access to the required components. These models are

truly impressive when optimized, yet still need

continual improvements to accurately cover a range

of human expressions.

Martinez B and Valstar M. F. (2021) investigate

responsive VR environments that are aware of the

emotional state of the user by means of real-time

facial expression recognition (FER) system. This

level of interaction is huge for gaming, with the

ability to dynamically improve virtual environments

by basing them off the emotions of the user; a horror

game could become scarier the more it detects fear,

for example, and a meditation space could alter based

on the amount of stress its owner is under. But latency

issues can break immersion if reactions aren’t

instantaneous, and hardware limitations could restrict

what can be processed in real time on consumer-grade

headsets. Since one of the ways in which emotions are

expressed is "culturally dependent" it leads to

challenges for the system, as subtle differences can

lead it to misidentify the user’s emotional state.

demonstrated impressive accuracy in controlled

settings, yet struggled with spontaneous expressions

like catching a fleeting smirk or a suppressed frown

revealing gaps between lab performance and

everyday use. Cultural nuances in emotion

expression also muddied results, since a smile in

certain contexts can signal politeness rather than

happiness. The competition bred innovation in deep

learning architectures, however, it indicated that the

height of emotional intelligence in a system needed

more than technical accuracy-it needed awareness of

human nuances.

Similar to our research in this domain, Lee, S et al.,

(2023) discovered substantial racial and gender biases

in commercial facial expression recognition (FER)

systems, showing that they generally misinterpret

emotions for darker-skinned people of all genders,

while achieving parity for lighter-skinned male faces.

The study shows how cultural differences at the

individual level affect training datasets that lean

toward Western expressions, as in the case of muted

happiness in some Asian cultures that, to a machine

learning program, would register as neutral

expressions. Then there are the technical limitations

that exacerbate these issues, resulting in poor lighting

for marginalized groups, whose faces are scanned

with less accuracy or at lower resolutions. While the

audit (maybe, well, auditish, you know, in general-

ish) proposes fairness metrics for comparing models,

unbiased systems would require not just nicer data

but, like, data set reform even, like what feelings

even counts as a category.

Figure 2: Sample Detection Pictures.

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Wang. Y and Guan. L (2022) also introduced an

emotion-aware music recommendation framework

facial expression, voice tone, and physiological

signals to realize personalized playlist generation,

just like when it plays all the peppy numbers when

you come back from work. While a multimodal

approach gives a fuller picture of emotional states

than single-input systems, real-world noise (such as

an interactive voice drowned out by the crowd or

ineffective image capture in low light) is often very

disruptive for its success. The model also fumbles

cultural differences between genres of music what

cheers one listener up might rub another the wrong

way exposing shortcomings in its emotional

intelligence. Privacy is another consideration since

biometric data plotting and monitoring raises ethical

questions about whether one could be secretly

monitored without consent. The system is impressive,

though, and a reminder that perfect mood-matching

isn’t just a matter of sensors; it should learn the

human behind the numbers.

3 METHODOLOGY

Figure 2 shows the Sample detection pictures. A

string of complex computer vision and audio

technologies work behind the scene to offer a

seamless transition from emotion to music.

Accounting for real-world aspects like variations in

lighting conditions and facial angles, we created a

rich dataset by merging a collection of 50,000 facial

expression images from FER-2013 dataset with

10,000 emotion-labelled songs. We use intelligent

pre-processing before the analyse, our hybrid detector

based on classical Haar Cascades and modern

MTCNN would reliably search for faces on images,

moreover, advanced mesh technique based on

MediaPipe finds 468 exact points that can be mapped

on face and algorithms balancing light spots.

Specifically, in order to have a more robust training

process, we synthetically increased our dataset with

computer-enhanced variations of expressions and

more difficult situations, including partial face

blocks.

Our dual-pathway AI architecture is where the

magic happens. First, the visual analysis pipeline

employs an efficient EfficientNet-B3 model for facial

expression understanding with additional sequence

aware GRU layers capturing the continuity of

emotion over time. At the same time, the audio

pathway analyzes musical features such as rhythm

and melody using commercial grade audio processing

tools. These two streams are fused via a particular

attention mechanism that identifies which modality

to trust more in a certain time step. The algorithm

checks that just because you expressed “happy”, that

could be more easily matched with 120-140 BPM pop

during the experience, or that “sad” expressions

should be followed with a slower ballad, 60-80 BPM.

The algorithm adapts and learns by different

strategies of sampling the space of songs to

incorporate new songs while honoring previous

preferences, and detects tiny drifts in emotion to

actively adapt the tracklist in real-time. Imagine it as

an intelligent version of the well-trained DJ who

reads the room, but this is being done with precision

and accuracy unique to AI.

To enable real-time responsiveness in the wild, we

devised a lean deployment architecture that

preserves accuracy while running light on consumer

devices. By dynamically quantizing the models and

skipping irrelevant emotional states, the system

achieves 30fps performance on mid-range

smartphones when processing the emotions. The

music engine, for instance, runs a music-preloading

buffer that predicts what will be the next best

emotional track to listen to, so that transitions are less

jarring as the user progresses through their emotional

landscape. The system uses gentle visual affordances

soft color gradients that reflect emotions the system

detects to make the experience transparent and elicit

emotion without bombarding users. Aftera series of

iterations and tests with various groups, we’ve been

able to make the system feel less like technology and

more like a friendly guide making an unexpected

detour, accompanying the user whenever they feel the

need, but this time more versed in their mood swings

throughout the day. Emotion-to-Genre Mapping:

• Happy → Upbeat Pop (120-140 BPM)

• Sad → Acoustic Ballads (60-80 BPM)

• Thompson sampling balances exploration vs

exploitation.

• Dynamic playlist restructuring based on

emotional trajectory.

3.1 Model Architecture

Our system as shown in figure 3 uses a complex

neural network that understands human emotion at

incredible nuance. [Layer 1: Spatial Analysis] The

first layer embodies the principles of spatial analysis,

with specialized convolutional networks that can be

thought of as digital artists, analysing the unique

curvature of each face. These networks catch the

subtleties of the language of emotion etched on faces

the way the eyebrows tingle with surprise; the lips

Real-Time Facial Emotion Detection Using OpenCV and CNN with Music Playback System

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tighten with frustration; the eyes crinkle warmly in

smiles of authentic joy.

Emotions are more than stills, they’re stories that

evolve over time. Enter temporal analysis, the

cutting-edge LSTM networks that play the role of

emotional narrators. These networks learn how

expressions change over segments, frame by frame,

and know that a quick smile may signal politeness

while a slowly widening grin typically indicates

sincere joy. They follow the beat of emotional

fluctuations, like turns of mood from momentary

irritation to prolonged sadness. these components

come together to build a holistic emotional

intelligence that serves as the bedrock of our music

recommendation system.

Figure 3: Model Structure.

3.1.1 Our Framework Addresses these

through

• A hybrid CNN-LSTM architecture for robust

temporal emotion tracking.

• Advanced image preprocessing pipelines

resilient to lighting artifacts.

• A multidimensional emotion mapping space

incorporating valence and arousal dimensions.

3.2 Perceived Features of Emotion-

Aware Music Systems

3.2.1 Perceived Usefulness (PU)

The value of an emotion-based music system is

evaluated as a necessity by which the better they can

build up the user experience over an ordinary music

player. Users assess whether the AI’s capacity to

sense emotions and modify music in real time gives a

tangible advantage over selecting a playlist by hand.

For instance:

• "Does this system make my music experience

more enjoyable?"

• "Is it better than scrolling through playlists

manually?"

Key Insight:

• Users prefer systems that reduce

effort while increasing personalization.

• Example: A stressed user receives calming

music automatically, eliminating the need to

search for "relaxing songs."

3.2.2 Relative Advantage (RD)

This measures whether the AI-based emotion-music

system offers clear improvements over conventional

methods (e.g., Spotify’s manual playlists).

Factors include:

• Speed: Instant mood detection vs. manual

input.

• Accuracy: Emotion-aware

recommendations vs. generic "Top 50"

playlists.

• Engagement: Dynamic responses (e.g., "You

seem excited! Playing upbeat tracks.") vs.

static interfaces.

User Perspective:

• "Why switch to this system? Because it

understands my mood faster than I can type

it."

3.2.3 Perceived Ease of Use (PES)

Users adopt technology when it feels intuitive and

effortless. For this system:

• Setup: Minimal steps just a webcam and

microphone.

• Interaction: No complex buttons; music

adapts automatically.

• Feedback: Simple messages like "Music

adjusted to your calm mood!"

Why It Matters:

• If the system feels "clunky," users revert to

manual controls.

• Example: A one-click calibration process

increases adoption rates.

3.2.4 Compatibility (CY)

Users assess whether the system aligns with their

habits:

• Tech-Savvy Users: Embrace AI-driven

features.

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• Traditional Listeners: May prefer familiar

apps (e.g., Spotify).

Bridging the Gap:

• Offer a hybrid mode (auto-detection +

manual override).

• Example: "You can still skip songs if the AI

misreads your mood."

3.3 Data Collection and User Testing

Methodology

To evaluate our emotion-aware music system, we

conducted comprehensive testing with 50

participants (aged 18-35) recruited from university

campuses and tech communities. The study aimed to

understand real-world usability, emotional

recognition accuracy, and music recommendation

satisfaction. Here's how we approached it:

3.3.1 Participant Demographics

• Age distribution: 62% 18-24 years, 38% 25-

35 years

• Gender split: 55% male, 45% female,

• Tech familiarity: 78% reported daily use of

AI-powered apps

3.3.2 Evaluation Metrics

1. System Performance:

• Emotion detection accuracy (per

facial expression)

• Music recommendation relevance

scores (1-5 scale)

• Average processing latency per

frame

2. User Experience:

• Daily engagement duration

• Manual override frequency

• Subjective satisfaction ratings

3.4 Mathematical Analyses

Our emotion-aware music system was rigorously

evaluated using a multi-layered analytical approach

combining machine learning validation and statistical

modeling. The CNN emotion detection model

underwent stratified 5-fold cross-validation,

achieving particularly strong performance on

happiness recognition (F1=0.94). We employed

multivariate regression to model key relationships,

finding system response time significantly predicted

user satisfaction (β=0.42, p<0.01), while

personalization frequency followed an inverted U-

curve with engagement. All analyses were conducted

using Python's SciPy ecosystem, with PyMC3

providing Bayesian uncertainty quantification and

bootstrap resampling (n=1000 iterations) ensuring

robustness. Figure 5 gives the results for Comparative

analysis of different model.

The analytical framework extended beyond

traditional methods by integrating real-time

performance metrics with longitudinal user behavior

patterns. Emotion detection accuracy was found to

influence music satisfaction via path analysis, and

music satisfaction was then tested via ANOVA

testing across hardware configurations to inform

optimization decisions. We then used Seaborn and

Matplotlib to visualize results, with power analysis

ensuring sufficient sample size.

4 MODEL IMPLEMENTATIONS

4.1 System Architecture and Workflow

The proposed framework (figure 4) integrates four

modular components to enable accurate face

emotion detection.

Figure 4: The Diagram of the Proposed Face Emotion Detection Model Based on Knowledge-Guided Cnn Network.

Real-Time Facial Emotion Detection Using OpenCV and CNN with Music Playback System

341

Figure 5: Comparative Analysis of Different Model.

We adopt a complementary multi-model framework

in our system to take advantage of deep learning

models in the context of emotion recognition. The

architecture integrates:

• Visual Feature Extraction: A convolutional

neural network carefully analyzes facial

features, capturing subtle expression nuances

such as micro-changes in facial muscles and

differences in eye openness. This spatial

analysis establishes vital emotional markers

down to pixel-level detail.

• Temporal Expression Analysis: Time-specific

recurrent nets equipped with memory cells track

changes in facial appearance across adjacent

video frames. This temporal operation captures

the trajectory of expressions, whether they are

transient responses or prolonged emotional

states.

• Intelligent Decision Fusion: A sophisticated

blending mechanism aggregates knowledge

from both networks, operating similarly to an

expert committee that considers multiple views.

This fusion layer balances the weight given to

spatial and temporal evidence based on the

clarity and length of expressions.

5 EXPERIMENTAL RESULTS

5.1 Performance Metrics

Figure 6: Emotion Class Distribution.

Table 1 shows the results obtained for the

performance metrics. Figure 6 shows the emotion

class distribution.

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Table 1: Performance Evaluation Metrics.

Condition Accurac

Latenc

Ideal Li

htin

91.2% 83 ms

Low Li

ht 87.6% 112 ms

With Sunglasses 79.4% 97 ms

5.2 User Experience Findings

• 78% reported improved mood regulation.

• 63% preferred system over static playlists.

• Average session duration increased by 22

minutes.

6 CONCLUSIONS

The impact of our emotion-aware music system on

human-computer interaction is significant,

combining advanced AI techniques with music - a

universal language. Real time emotion detection and

responding to emotion of user by carefully curated

playlists is also what makes the system a truly

tailored listening experience that adapts to users

mood. Positive participant feedback-reporting

feelings of being understood and engaged-confirms

that when designed with empathy and precision,

technology can enhance emotional well-being. This

project shows how machine learning can be more

than cold calculations, it can be warm, human

experiences.

Excitingly, these applications are only the tip of

the iceberg. Therapeutic clinics to smart homes: The

potential applications of emotion-aware systems

could transform our relationships with the technology

that surrounds us every day. Aspects, such as

inconsistency in lighting and computational burden

still exists this paper helps to build a foundation

through to an exhilarating future where tech does not

only serve us, but where it actually comprehends us.

As the system continues to improve, our vision

remains simple: AI that plays music for you, the right

music at the right time, making each listener feel

acknowledged, valued, and heard.

Looking ahead, the goal is to make our real-time

emotion detection system more intuitive, in fact,

we’ll integrate facial cues with voice tones for

enhanced accuracy, such as identifying sarcasm or

suppressed emotions. And since we optimize the

model for edge devices, we can process the data in a

much faster way and more privacy-aware, as we do

not send the data to the cloud. We are also placing a

strong emphasis on inclusivity being trained on a rich

set of data from around the world, in order to lessen

cultural biases in emotion interpretation. Interactive

features, such as real-time feedback while on video

calls, will help users improve their emotional

expressiveness. Finally, we’re exploring AR overlays

to gamify therapy sessions and adding adaptive

learning so the AI evolves with users’ unique

expressions over time.

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